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Energy Analysis, Diagnostics, and Conservation in
Semiconductor Manufacturing
Yogesh Mukesh Mardikar
Thesis submitted to the
College of Engineering and Mineral Resources
at West Virginia University
in partial fulfillment of the requirements
for the degree of
Master of Science
In
Industrial Engineering
B. Gopalakrishnan, Ph.D, PE, Chairman
Robert Creese, Ph.D.
Dimitris Korakakis, Ph.D.
Department of Industrial and Management Systems
Engineering
Morgantown, West Virginia
2004
Keywords: Semiconductor Manufacturing, Process Energy, Support
Energy, Oxidation, Doping
ABSTRACT
Energy Analysis, Diagnostics, and Conservation in Semiconductor
Manufacturing
Yogesh Mukesh Mardikar
Semiconductor industry accounts for 1.3% - 2% of the total US electricity
consumption in the manufacturing sector. Energy in the form of electricity is required to
operate the manufacturing process equipment, maintain the clean room conditions, and
operate equipment such as Heating Ventilation and Air conditioning (HVAC) units, and
Chillers. The process equipment accounts for 40% of the operating costs in a
semiconductor fabrication unit [8]. Since a significant amount of energy is used by the
manufacturing process, it becomes necessary to determine process parameters which
govern energy. A computer based interactive model referred to as Semiconductor Energy
Calculation Program (SECALPRO), was built in this study to estimate the energy
requirement of any particular process in semiconductor manufacturing based on the input
variables. Energy intensive processes such as layering and diffusion are studied. Process
parameters most sensitive to energy are determined. It is intended to enable the estimation
of process energy beforehand by analysis of process parameters governing energy. The
computer model also estimates the support energy requirement in semiconductor
manufacturing. It is found that process temperature is the key variable governing the
energy requirement of the processes under study. This research reports a sensitivity
analysis of process variables with respect to energy. A research in this area will help the
production managers in the semiconductor fabrication facilities to effectively select the
production parameters based on the results obtained by this research.
ACKNOWLEDGEMENT
I would like to wholeheartedly thank my advisor Dr. B. Gopalakrishnan for his
continued support, guidance and encouragement during the course of this research work. I
also wish to thank Dr. Robert Creese and Dr. Dimitris Korakakis for their advice and
support.
Above all, I wish to thank god, my parents, and all my friends for their constant
support and blessings and enabling my success and happiness in all my pursuits and
endeavors in life.
iii
Table of Contents
ABSTRACT........................................................................................................................... ii
ACKNOWLEDGEMENT .................................................................................................... iii
1
Introduction.................................................................................................................... 1
1.1
Electronics Sector and Energy ............................................................................... 1
1.2
Semiconductor Manufacturing Process ................................................................. 2
1.2.1
Wafer Fabrication .......................................................................................... 2
1.2.2
Semiconductor Fabrication ............................................................................ 4
1.2.3
Assemblies, Encapsulation and Packaging .................................................... 6
1.3
The Semiconductor Industry Analysis................................................................... 7
1.4
Economic Profile and Trends................................................................................. 8
1.5
Energy Use in Semiconductor Industry ............................................................... 11
1.6
Energy Intensity ................................................................................................... 13
1.7
Cogeneration ........................................................................................................ 15
1.8
Need for Research................................................................................................ 15
1.9
Research Objectives............................................................................................. 16
1.10 Conclusion ........................................................................................................... 17
2
Literature Review......................................................................................................... 19
2.1
General Energy Management Activities.............................................................. 19
2.2
Productivity Improvement Activities in Electronics Sector ................................ 23
2.3
Energy Efficiency Initiatives and Best Practices in Electronics Manufacturing . 23
2.4
Conclusion ........................................................................................................... 25
3
Research Approach ...................................................................................................... 26
3.1
Methodology ........................................................................................................ 26
3.2
Oxidation/Layering .............................................................................................. 26
3.3
Thermal oxidation process................................................................................... 28
3.4
Oxidation process parameters sensitive to energy ............................................... 29
3.5
Deal Grove Model................................................................................................ 32
3.6
Doping.................................................................................................................. 35
3.7
Predeposition........................................................................................................ 36
3.8
Drive-in oxidation................................................................................................ 36
3.9
Dopant sources..................................................................................................... 37
3.10 Diffusion process parameters sensitive to energy................................................ 37
3.11 Diffusion model ................................................................................................... 39
3.12 Conclusion ........................................................................................................... 41
4
Model Development..................................................................................................... 43
4.1
Purpose of Modeling............................................................................................ 43
4.2
C ++, SECALPRO Model Programming Language............................................ 43
4.3
SECALPRO Model Assumptions........................................................................ 43
4.4
SECALPRO Model Development ....................................................................... 44
4.4.1
Layering/Oxidation Process flowchart ........................................................ 44
4.4.2
Diffusion/Doping Process flowchart............................................................ 45
4.5
Areas of significant energy consumption ............................................................ 46
4.6
Systematic approach to estimate energy consumption ........................................ 46
4.7
Flowchart Description.......................................................................................... 50
iv
4.7.1
Process selection .......................................................................................... 50
4.7.2
Input variables/ Process related ................................................................... 51
4.7.3
Horizontal Tube Furnace ............................................................................. 54
4.7.4
Input variables/ Energy related .................................................................... 57
4.7.5
Furnace/Oxidizer specifications................................................................... 60
4.7.6
Energy consuming elements in an Oxidizer/Diffuser/Furnace.................... 60
4.7.7
Support equipment energy estimation ......................................................... 63
4.7.8
Clean Room design ...................................................................................... 64
4.7.9
Total kW and kWh consumption ................................................................. 68
4.8
Conclusion ........................................................................................................... 68
5
Results and Sensitivity Analysis.................................................................................. 69
5.1
Oxidation.............................................................................................................. 69
5.1.1
Temperature effect ....................................................................................... 69
5.1.2
Throughput effect......................................................................................... 78
5.1.3
Ramp rate Effect .......................................................................................... 81
5.2
Doping.................................................................................................................. 83
5.2.1
Dopant Effect ............................................................................................... 83
5.2.2
Process time effect ....................................................................................... 84
5.2.3
Process temperature effect ........................................................................... 85
5.3
Summary of major findings ................................................................................. 89
5.4
Conclusion ........................................................................................................... 90
6
Model Validation ......................................................................................................... 91
6.1
Validation of Model............................................................................................. 91
6.1.1
Furnace energy consumption ....................................................................... 93
6.1.2
Clean room design ....................................................................................... 94
6.1.3
Total oxide growth time............................................................................... 95
6.2
Important facts revealed after personal communication...................................... 96
6.3
Conclusion ........................................................................................................... 96
7
Conclusion and Future Work ....................................................................................... 98
7.1
Conclusion ........................................................................................................... 98
7.2
Future work........................................................................................................ 102
7.2.1
Linear programming approach................................................................... 102
Reference ........................................................................................................................... 106
Nomenclature..................................................................................................................... 109
Appendix I ......................................................................................................................... 110
SECALPRO Model Screenshots........................................................................................ 110
Appendix II ........................................................................................................................ 112
List of variables.................................................................................................................. 112
Appendix III....................................................................................................................... 115
Source code........................................................................................................................ 115
v
List of Figures
Figure 1.1: Semiconductor manufacturing process flow chart .............................................. 3
Figure 1.2: Integrated circuit development............................................................................ 8
Figure 1.3: Worldwide end use of semiconductors .............................................................. 9
Figure 1.4: Integrated circuit unit and revenue growth ......................................................... 9
Figure 1.5: Worldwide semiconductor shipments ............................................................... 10
Figure 1.6: Percent market share ......................................................................................... 10
Figure 1.7: Operating costs of a clean room plant .............................................................. 12
Figure 1.8: Annual energy consumption for semiconductor fabrication units in California12
Figure 1.9: Semiconductor manufacturing energy balance ................................................. 14
Figure 1.10: Breakeven analysis for $/kWh rates possible by cogeneration ....................... 15
Figure 1.11: System schematic ............................................................................................ 17
Figure 3.1: Oxide layer formation ....................................................................................... 27
Figure 3.2: Formation of doping barrier .............................................................................. 27
Figure 3.3: Surface dielectric formation .............................................................................. 28
Figure 3.4: Oxide layer formation ....................................................................................... 29
Figure 3.5: Crystal orientation of silicon wafer ................................................................... 31
Figure 3.6: Oxide growth dynamics..................................................................................... 32
Figure 3.7: Predeposition process ........................................................................................ 36
Figure 3.8: Drive-in process ................................................................................................ 37
Figure 3.9: Dopant concentration vs. junction depth – Error function curve ...................... 40
Figure 3.10: Dopant concentration vs. junction depth – Gaussian curve ............................ 41
Figure 4.1: Oxidation/Layering process flowchart .............................................................. 45
Figure 4.2: Diffusion/Doping process flowchart ................................................................. 46
Figure 4.3: Flowchart to determine energy consumption .................................................... 47
Figure 4.4: Schematic of furnace tube and heating coils ..................................................... 55
Figure 4.5: Horizontal tube furnace along with cleaning station......................................... 57
Figure 4.6: Elements of four tube horizontal furnace .......................................................... 61
Figure 4.7: Temperature Levels for Oxidation process. ...................................................... 62
Figure 4.8: kW consumption vs. time per process cycle. .................................................... 63
Figure 4.9: Support equipment used in semiconductor manufacturing ............................... 63
Figure 4.10: Air changes per hour for a particular clean room Class .................................. 65
Figure 4.11: Continued clean room layout .......................................................................... 66
Figure 5.1: Time vs. temperature – 0.1 micron, 900 – 950 °C ............................................ 70
Figure 5.2: Percentage energy distribution – 0.1 micron, 900 – 950 °C.............................. 72
Figure 5.3: Energy (kWh) consumption per wafer vs. temperature – 0.1 micron oxide ..... 73
Figure 5.4: Time vs. temperature –1 micron, 1000 – 1150 °C ............................................ 74
Figure 5.5: Percentage energy distribution – 1 micron, 1000 – 1150 °C............................. 76
Figure 5.6: Energy (kWh) consumption per wafer vs. temperature – 1 micron oxide ........ 76
Figure 5.7: Time vs. temperature – 0.25 micron, and 950 – 1050 °C ................................. 77
Figure 5.8: Time vs. temperature – 0.5 micron, 1000 – 1150 °C ........................................ 78
Figure 5.9: Throughput vs. temperature – 0.1 micron oxide growth................................... 80
Figure 5.10 Throughput vs. temperature –1 micron oxide growth ...................................... 80
Figure 5.11: Time vs. temperature – 1 micron, 8 ºC/min, 10 ºC/min, 12 ºC/min RUR....... 82
Figure 6.1: Power (kW) consumption vs. time – facility furnace........................................ 93
vi
Figure 6.2: Power measurement using power logger........................................................... 94
Figure 6.3: Furnace loading/unloading station in Class 10 and reactor section in Class .... 94
Figure 6.4: Clean room classification used in the SECALPRO model ............................... 95
Figure 7.1: Energy usage distribution – 1 micron oxide, 1150 °C ...................................... 99
Figure 7.2: Energy usage distribution – N type dopant phosphorus, 1150 °C .................. 101
Figure 7.3: Temperature vs. time – Furnace cycle............................................................. 104
Figure I.1: Screenshot of the SECALPRO model - Introduction ...................................... 110
Figure I. 2: Screenshot of the SECALPRO model - Execution......................................... 110
Figure I.3: Screenshot of the SECALPRO model - Execution.......................................... 111
Figure I.4: Screenshot of the SECALPRO model – Results (1 micron, 1150 °C, wet,
<100>)........................................................................................................................ 111
vii
List of Tables
Table 1.1: Variations in percentage energy use in semiconductor fabrication units ............. 2
Table 1.2: Level of integration............................................................................................... 8
Table 1.3: Semiconductor industry statistics for year 1997................................................... 9
Table 1.4: Texas State Fabrication units enlisting product type and throughput per month 11
Table 1.5: Electronics sector electricity and natural gas consumption for California State 13
Table 4.1: Growth rates and activation energy .................................................................... 52
Table 4.2: Furnace/Oxidizer specifications ......................................................................... 60
Table 4.3: kVA and kW consumption of furnace elements ................................................ 62
Table 4.4: Support equipment power ratings ...................................................................... 63
Table 5.1: Results – 0.1 micron, 900 – 950 °C .................................................................... 71
Table 5.2: Results –1 micron, 1000 – 1150 °C .................................................................... 75
Table 5.3: Energy savings and productivity improvement for 0.25 micron oxide growth,
950 – 1050 °C .............................................................................................................. 77
Table 5.4: Energy savings and productivity improvement for 0.5 micron oxide growth.... 78
Table 5.5: Ramp up and ramp down rates used in the SECALPRO model......................... 81
Table 5.6: Effect of ramp up rate on energy savings and productivity improvement – 1
micron oxide growth .................................................................................................... 82
Table 5.7: Diffusion coefficient and activation energy values for P – type, and N – type
doping .......................................................................................................................... 84
Table 5.8: Effect of dopant type on junction depth ............................................................. 84
Table 5.9: Effect of drive-in time on junction depth for P type and N type doping ............ 85
Table 5.10: Temperature and drive - in time effect on junction depth ................................ 85
Table 5.11: Temperature effect on drive - in time and energy usage .................................. 86
Table 5.12: Results –P type dopant, drive-in time 2.6, 5, 10 hours, and temperature 1100 1150 °C ........................................................................................................................ 87
Table 5.13: Results – N type dopant, drive-in time 1.91, 5, 10 hours, and temperature 1100
- 1150 °C...................................................................................................................... 88
Table 6.1: Tube furnace specification.................................................................................. 92
Table 6.2: Wafer cleaner specification ................................................................................ 92
Table 6.3: Sample oxidation process ................................................................................... 92
Table 6.4: Sample doping process ....................................................................................... 92
Table 6.5: Clean room specification .................................................................................... 94
Table N.1: Nomenclature for equations............................................................................. 109
Table AII.1: Variable data type and description................................................................ 114
viii
Chapter 1
1 Introduction
1.1
Electronics Sector and Energy
Electronics sector as a whole involves manufacture of semiconductor devices,
electron tubes, computers, printed circuit boards, capacitors, resistors, transistors and
miscellaneous electronic devices. Manufacturing technology had changed since the
development of electron tubes in early 1900’s to the present day ultra large scale
integration era. Although the above ancillary industries in electronics sector produce
altogether different end products, but they definitely share something in common. That
common characteristic is the energy intensity in these units. Restricting the study to
semiconductor manufacturing has revealed that the semiconductor industry is the largest
end user of energy. Major energy consumption is in the area of process operation and
supporting utilities. Improvement in technology demands changes in the current facilities.
Almost every three to five years, the semiconductor technology demands for larger wafer
dimensions. Present day fabrication units are thus modified to accommodate changes in
technology. Here major attention is given on the new technology production facility.
Energy aspect, though important is thus sidelined. A study done by Environmental
Protection Agency (EPA) has revealed that energy use in semiconductor fabrication units
(Fabs) is not consistent. As seen in Table 1.1, Fab5, Fab6, and Fab7 process almost equal
number of wafers per month, but their energy use varies from 60% to 90%. Many other
studies done on present day Fabs show same pattern of results. Thus it becomes necessary
to analyze the elements causing this large variation in energy use. Some of the biggest
manufacturers demand more than 100 million kWh of energy per year [1]. Here lies an
opportunity to find reasons causing this high energy demand. Successful analysis will result
in tremendous energy savings. Pollution control is also a major concern for any
manufacturing sector. Also, reductions in emissions will be the added advantage.
Development in electronics sector was so fast that it was almost neglected as a
candidate for implementing energy efficiency measures. Study by USDOE has shown the
fact that traditional heavy industry like Steel and Iron, Chemical, and Pulp and Paper have
almost declining or steady energy intensities. Whereas it is estimated that electronics sector
will be one of the highest energy intensive industries in the coming future. Hence, an effort
1
is made to study the energy use in semiconductor industry – a largest end user of energy.
Basics of semiconductor manufacturing are discussed further.
Fabrication Units
Fab1
Fab3
Fab4
Fab5
Fab6
Fab7
Fab9
Fab10
Wafers/month
HVAC, Chillers
(% of total energy)
Mfg. Process
Equipment
(% of total energy)
15,000
20,000
19,500
21,700
21,700
21,700
30,000
30,300
35%
46%
31%
39%
32%
35%
54%
37%
38%
40%
42%
51%
32%
36%
18%
33%
Table 1.1: Variations in percentage energy use in semiconductor fabrication units [1]
1.2
Semiconductor Manufacturing Process
The semiconductor manufacturing process starts with the extraction of raw
materials from the earth followed by its purification. The most commonly used
semiconductor material is silicon. The silicon used needs to be refined and is called as
semiconductor grade silicon (SGS). This also involves reducing the impurities to the
specified level. The refining or purification process is the transformation of ore to silicon tri
chloride a silicon bearing gas. These gases are further reacted with hydrogen to produce
SGS, which has a crystalline structure [2]. The Semiconductor manufacturing process is in
three major steps viz. wafer fabrication, semiconductor fabrication, and assembly –
packaging. The semiconductor manufacturing process is outlined in Figure 1.1.
1.2.1
Wafer Fabrication
The first step in wafer fabrication is to have a perfect crystal structure. By perfect, it
means the crystallized structure silicon has to have a specific orientation. Different planes
of a crystal have different electrical, chemical properties depending on the binding pattern
of the atoms in that specific plane. A method known as Miller Indices is used to define the
planes, which identify the xyz location of each plane in a crystal [3].
The crystal obtained has a polycrystalline structure and is not doped. “The process
of converting the polycrystalline structure to a large crystal of single crystal structure, of
the correct orientation, and containing the proper amount of dopant is called “crystal
growing”. Czochralski method [3] is most commonly used for growing crystals. The
furnace used has a crucible, which is heated by coils. The polycrystalline chunk with
proper quantity of dopant is heated in the crucible to produce an N or P type crystal.
2
Crystal Growth
Polycrystalline Silicon
Crucible Furnace
Ingot
Crystal
Inspection
Rejected Ingot
Ingot Facing, Grinding, Slicing, Polishing
Oxidation
Patterning
Photos resist Coating
Etching
Photo Resist Removal
Doping
Heat Treatments
Die Separation and Pickup
Die Attach
Wire Bonding
Encapsulation or Sealing
Chips
Figure 1.1: Semiconductor manufacturing process flow chart
A seed crystal of the desired orientation is positioned in such a way that it barely
touches the molten polycrystalline chunk. It is then slowly raised above the melt. This is
the point when the crystal growth starts. The phenomenon of surface tension causes the
melt to adhere to the seed. The crucible and the seed are rotated in opposite directions to
3
achieve uniformity in doping and perfection in crystal. An ingot is then ready to be used for
further processes [3].
Semiconductor applications demand a very high perfection level of the crystals
from which they are made. General defects observed in crystals are the point defects,
dislocations, and the growth defects. The crystal is inspected based for the defects if any
[3]. The ingot produced in the crucible has tapered ends, which are removed by a saw. The
diameter of the ingot is not constant throughout its length. These variations in diameter
should be removed so as to facilitate easy handling of ingot for further processing. A
center-less grinder is used to perform this operation [3]. Further the crystal is checked for
its electronic properties such as resistivity and conductivity. Also it is checked for its
orientation in which the ends of the crystal are etched first. The crystal is then placed in
refraction equipment and a collimated light is reflected from the crystal surface onto a
screen. The pattern obtained on the screen represents the actual pattern of the crystal
orientation [3].
After crystal checking, the ingot is ground along its axis similar to a flat. The flat
position represents the orientation of the wafer. The ingot is then sliced into wafers by
using a circular diamond saw with inside diameter as the cutting edge. Wafer slicing
damages the wafer surface to an extent which can be overcome by polishing. Rough
polishing is an abrasive action to remove these irregularities caused by the slicing process.
In final polishing the wafer is etched while it is rotated. This causes a thin etchant film to
develop on the wafer surface, which is further removed by a buffing process. A high degree
of flatness is thus obtained after the final polishing. Further the edge of the wafer is ground,
which smoothen the edge and reduces the possibility of edge chipping and wafer damage.
The finished wafers are oxidized i.e. a very thin coat of oxide is formed on the surface
before they are used for wafer fabrication. This protects the wafer from damage while they
are transported to the fabrication section.
1.2.2
Semiconductor Fabrication
The wafer fabrication process comprises of four major operations [3]:
1. Layering/Oxidation
2. Patterning
3. Doping
4
4. Heat Treatment
Layering is the process of either growing or depositing layers of materials on the
wafer surface. These layers can be grown by oxidation process in which the silicon wafer is
exposed to high quality oxygen. Chemical vapor deposition is a deposition technique,
which deposits a film on the wafer surface by a chemical reaction of a gaseous mixture.
Patterning also referred as photolithography follows layering. “In photolithography
the required circuit pattern is first formed in photo masks and transferred onto the surface
layer(s) of the wafer through the photo masking steps [3]. The detailed circuit pattern is
obtained by applying several times the photo mask layers. It is a sequential process, which
undergoes:
a. Photo resist coating
b. Etching
c. Photo resist removal
In this process a layer of coat, resistive to etchant is applied on the oxidized layer of
wafer. The mask with circuit details is then aligned perfectly to the wafer layer and is
exposed to ultraviolet light. The mask protects the photo resist portion of the layer, which
need not be developed. The exposure of photo resist to ultraviolet light changes the
properties of resist from a soluble to an insoluble one. The soluble portion is the portion
covered with mask. Developers (chemical solvents) are then used to remove the soluble
portion of the photo resist. The portion of the wafer with photo resist coating developed
dissolves in the etchant. This results in the required circuit pattern on the wafer surface.
The photo resist coating is then removed so that a new layer can be applied again to
obtain the desired circuit pattern on the wafer surface. This process of photo resist
application, etching, and removal is repeated until the final circuit is obtained. The wafer
needs to be doped by impregnating dopants in the wafer surface. This is done to create Ntype or P-type regions in the wafer surface which further act as regions for operation of
circuit components like resistors, capacitors. Doping can be done by thermal diffusion or
by ion implantation. Thermal diffusion is the process of heating the wafer around 1000 º C
and then exposing to dopant vapors. In ion implantation, the ionized dopant atoms are
accelerated and shot on the wafer surface. The crystal structure is disrupted by the ion
implantation in the doping process. Heat treatment is heating and cooling the wafer to
5
relieve any stresses and deformations caused during previous operations. This process is
carried out at 1000 ºC and known as annealing.
1.2.3
Assemblies, Encapsulation and Packaging
The dies or individual chips are tested for their electrical properties by using special
probes connected to a programmable computer. The good and bad dies are sorted by some
identification marks. The wafer is now ready for the packaging process [3].
1. Backside preparation
2. Die separation and pickup
3. Die attach
4. Wire bonding
5. Encapsulation or sealing
6. Lead plating and trimming
7. Package marking and final testing
The wafer undergoes different processes in the wafer fabrication. Some of the
processes cause damage to the rear side of the wafer. These imperfections are removed by
removing a thin layer of rear surface either by back grinding, chemical - mechanical
process like polishing, or etching. Thicker dies need bigger packages. Also, in the die
separation process, the saw needs to cut the material to even lower depths. All of these
imperfections can thus be eliminated by backside preparation [3].
The die sort process separates good and bad dies on the wafer surface. The good
dies are then cut through the wafer and placed on a carrier plate. Die separation can be
achieved by either using a diamond scriber or by using a diamond saw. The scribing and
sawing actions are partially done and are followed by die stressing. The stressing is
accomplished by rolling a cylindrical roller on the wafer surface. Further the good dies are
attached to the package and this process of attaching a die to the package is termed as Die
attachment. This process acts like a permanent physical bond between the chip and the
package; it serves as a conducting or insulating medium in the electrical circuit, and as a
heat dissipation medium from the chip to the package. Once the dies are attached, the next
process is wire bonding. Wire bonding is the process of making electrical connections
between the chip-bonding pad and the inner leads of the lead frame to connect the chip to
the leads. Different methods like thermo-compression, thermo-sonic, and tape automated
6
bonding are used depending on the wire material. Commonly used wire materials are gold
and aluminum for their good electrical conductivity and ductility [3].
The chip is now ready for encapsulation. It encloses the chip in a protective
enclosure. The enclosure can either be made of metal, ceramic, or epoxy. In metal
enclosures like metal cans, the sealing is achieved by welding the package base to the cover
metal lid. In ceramic and epoxy enclosures, the base package along with the top cover are
passed through a conveyor furnace or placed in an oven at a temperature ranging from 300400 °C. Encapsulation is followed by lead plating and trimming. It is the process of plating
the leads of the package by tin, solder or gold. This increases the solder ability of leads to
the application like printed circuit boards. The plated leads are then trimmed properly. At
last, the finished chip is marked with specific information like product type, specifications,
lot size. The final chip is then tested for electrical, functional, and physical properties. Once
it is cleared through the inspection, the chips are packed and ready for shipping [3].
1.3
The Semiconductor Industry Analysis
The semiconductor industry is one of the major developing industries in the
manufacturing sector. Since 1950, it has become a key for advancement. The nation’s
technological growth is believed to be related with its development in the electronic sector.
With the small scale integration era which had 50 components per chip at the maximum has
changed to more than 1 million components per chip. Over a period of 50 years, the
technology has improved a thousand times. The US semiconductor industry is a $150
billion dollar plus enterprise with an expected growth rate of 10 % for the period 2001 to
2005. The computers and telecommunications sector itself demand 65% of the
semiconductor market share for the year 2001 [7]. As can be seen in Table 1.2 and Figure
1.2, the number of components per chip for the small scale integration (SSI) era was
limited to a maximum of 50. On the other hand, ultra large scale integration required use of
over 1,000,000 components per chip.
7
Level
SSI
MSI
LSI
VLSI
ULSI
Components per chip
2 - 50
50 - 5000
5,000 - 100000
100,000 – 1,000,000
>1,000,000
Table 1.2: Level of integration [3]
Components per chip
1,000,000 1,500,000
10,000,000
100,000
1,000,000
100,000
5,000
10,000
1,000
50
100
10
1
SSI
MSI
LSI
VLSI
ULSI
Components per chip
Figure 1.2: Integrated circuit development
1.4
Economic Profile and Trends
The US semiconductor industry employs 198,000 workers around the country, with
a 100,000 production task force. There were approximately 980 companies in the US alone
in 1997, manufacturing semiconductors of different varieties and specifications as against
823 companies in 1992; a 19% growth. The shipments reached a value of $78 billion as
against $33 billion, a 135 % growth. Figure 1.3 shows the worldwide end use of
semiconductors. The major consumers are the computers and the communications industry
[4]. The hourly wages of production workers were $16.36 in the year 1997. The statistics
are shown in Table 1.3 [5].
8
Figure 1.3: Worldwide end use of semiconductors [7]
Companies
Employment
Production workers
Value of shipments
Production worker hourly wage
980
198,119
105,781
78 billion dollars
$16.36
Table 1.3: Semiconductor industry statistics for year 1997 [5]
From Figure 1.4, it is evident that integrated circuit (IC) manufacturing industry is growing
Figure 1.4: Integrated circuit unit and revenue growth [4]
9
each year at a rapid pace. Also, apart from the global competition, the industry has
managed to maintain the average revenue level.
The worldwide semiconductor shipments reached an all time high value of $200 billion in
the year 2000 with US market share of 70% as can be seen in Figure 1.5 and Figure 1.6
respectively.
Worldwide Semiconductor shipments
Billions of Dollars
250
200
150
100
50
0
90
91
92
93
94
95
96 97
Year
98
99
00
01
02
Figure 1.5: Worldwide semiconductor shipments
American Market Share
Percent Market share
80
70
60
America
50
40
Japan
30
Other
20
10
0
91
92
93
94
95
96
97
98
99
Year
Figure 1.6: Percent market share
10
00
01
Table 1.4 enlists some of the major semiconductor fabrication units in Texas State. It can
be seen that National semiconductors manufacture 44,000 wafers/month [6].
Table 1.4: Texas State Fabrication units enlisting product type and throughput per month [6]
1.5
Energy Use in Semiconductor Industry
Semiconductor industry accounts for 1.3% - 2% of the total US electricity
consumption in the manufacturing sector. Energy in the form of electricity is required to
operate the process equipment, maintain the clean room conditions, Heating ventilation and
air conditioning units, and chillers. The energy information administration a part of
Department of Energy released a statistical report on energy consumption by US
manufacturing sector for the year 1998. It revealed that about 46 Trillion Btu’s of energy is
consumed by the Semiconductor Industry [7].
A typical semiconductor factory uses electricity which would at least serve 7,000 to
8,000 homes [8]. Also, it is a major consumer of natural gas. For the year 1998, the US
11
semiconductor industry consumed around 19 billion cubic feet (0.25 % of total 7,231
billion cubic feet) of natural gas [7].
Opertaing Costs
Support
3%
DI w ater
5%
Process Tools
39%
Proces s w ater pumps
4%
Proc ess Tools
Nitrogen Plant
5%
Chillers and Pumps
Rec irc. and Make-up Fans
Fans
Fans
6%
Nitrogen Plant
Process water pumps
DI water
Recirc. and Make-up
Fans
17%
Chillers and Pumps
21%
Support
Figure 1.7: Operating costs of a clean room plant [8]
The process equipment contributes to around 40% of the operating costs as shown
in Figure 1.7. The Heating ventilation and air conditioning units take the heat released by
the process equipment and occupants to maintain the clean room conditions. They account
for 40% - 45% of the total costs.
Figure 1.8: Annual energy consumption for semiconductor fabrication units in California State [10]
12
Most of the semiconductor fabrication units are located in California. As per the California
energy commission, the state had the highest energy consumption of 800 GWh in the year
1997 for San Francisco alone as shown in Figure 1.8 [10]. As seen in the Table 1.5, the
semiconductor industry is the highest consumer of electricity and natural gas in California
State. It accounts for 1,527 million kWh for the year 1997 which is 1,527 GWh [10].
CA Electronics Industry Energy
Consumption 1997
Electricity (million
KWh)
Natural Gas (million
Therms)
Electron Tubes
94.285
0.968
Printed Circuit Boards
410.182
3.324
1,527.413
23.596
Electronic Capacitors
26.801
1.546
Electronics Resistors
2.498
0.009
Electronic Connectors
65.875
0.371
Misc. Electronics Components
504.204
10.774
Electronic Computers
373.123
8.773
Computer Storage devices
468.036
1.479
8.826
0.485
Misc. Computer Peripheral equip.
146.999
0.753
Telephone and Telegraph equip.
222.118
0.957
Radio and TV Broadcasting Equip.
449.243
1.987
4,299.603
55.022
Industry
Semiconductors & Related devices
Computer Terminals
Total
Table 1.5: Electronics sector electricity and natural gas consumption for California State [10]
1.6
Energy Intensity
The energy intensity in semiconductor manufacturing varies based on the process.
Some processes use high energy consuming equipment and some use medium to low
energy consuming equipment but with high precision. As seen in the flowchart in Figure
1.9, the processes under bold dotted boxes represent high energy consuming processes.
Elements in the faint dotted boxes and continuous line boxes represent medium and low
energy consuming processes respectively. Processes like crystal growth and ingot
formation, oxidation, and doping use significant amount of energy. These involve high
energy end use equipment such as furnaces. Figure 1.9 also describes the energy balance in
semiconductor manufacturing. For example, in the third step i.e. oxidation/layering, electric
13
energy is used by the furnace as input and hot exhaust gases exit the furnace from the stack.
It also involves use of gases such as O2, H2, N2, and Steam.
Polycrystalline Silicon
Dopants
1
Electricity
Crucible Furnace
Stack
Ingot
Electricity
Scrap
Ingot Facing, Grinding, Slicing,
Polishing
2
Scrap
Wafers
Electricity
Stack
Oxidation
Diffusion Furnace
300 – 1200 ° C
3
O2, H2, N2, Steam
Electricity
4
Chemicals
Patterning
Scrap
Photos resist Coating
Etching
Heat
Photo Resist Removal
Doping
Diffusion Furnace
5 900 – 1200 ° C
Electricity
Dopants
Wafers
Scrap
6
Heat Treatments
Energy Intensity
High
7 Die Separation and Pickup
Electricity
Medium
8 Die Attach
Low
9 Wire Bonding
10
Encapsulation or Sealing
Chips
Figure 1.9: Semiconductor manufacturing energy balance
14
1.7
Cogeneration
Cogeneration is done by most of the semiconductor device manufacturers.
Semiconductor industry is strongly dependent on high power quality and reliability. Recent
blackouts in California and northeastern states have raised the need for onsite power
generation. Some of the advantages of cogeneration would be control over the own power
plant to increase reliability, constant cost for power over a long term, and control over
power quality. Advanced micro devices, a semiconductor fabrication unit has cogeneration
plant at their manufacturing site. Onsite power generation helped reduce the annual CO2
emissions by 20,000 metric tons. Texas Instruments also realized reduction in power costs
by 30% [11]. “A study by the United States Federal Government for their facilities showed
that cogeneration became feasible for all types of machines at about $0.15/kWh as seen in
Figure 1.10” [11].
Figure 1.10: Breakeven analysis for $/kWh rates possible by cogeneration [11]
1.8
Need for Research
As stated earlier that the process equipment account for 40% of the operating costs
for a semiconductor fabrication unit. The minimum energy use for processing an 8 inch
wafer by implementing the energy efficiency measures would not be less than 450 kWh
15
[8]. This amount accounts for energy used by the equipment, the HVAC system for
cleanroom, and de-ionized process water. Since a significant amount of energy is used by
the process equipment, it becomes necessary to determine process parameters which define
energy. Once the parameters are defined, sensitivity analysis could be done to study the
effect of change in parameters with respect to energy. Sufficient amount of research is
being done on the HVAC and plant clean room requirements. Efforts are made to estimate
the energy consumed by the process equipment/manufacturing process, but no effort is
done to specifically determine the production parameters and analyze its effects on energy.
Present day energy estimation methods use power meters to monitor the power
consumed by equipment for a specific period of time. The methods used are risky and not
safe enough to be carried out by any operator and also consume a lot of time. An electrician
will be required to connect all the current and voltage probes to the control panel of the
equipment. These methods can only estimate the energy but cannot provide information on
production parameters responsible for it. Semiconductor fabrication units cannot afford to
have shutdowns even for a short duration of time. As compared to the other manufacturing
industries, here the process uses a significant amount of support energy in the form of
HVAC and clean rooms. The technology used in electronics sector is changing rapidly,
almost every six months. In order to be up to this mark so as to accommodate changes in
technology, the fabrication units undergo severe changes in infrastructure almost every
year or a two. Fierce competition amongst the fabrication industries nationally as well as
internationally demand for even lower costs. Hence, continuous operating cost reduction is
necessary to make the payback on these high investments attractive. Here lies an
opportunity to study and analyze the manufacturing process which is a key component of
operating cost in a semiconductor fabrication unit. Hence, an investigation in this area will
help the production managers in the wafer fabrication facilities to effectively use the
process equipment based on the results obtained from this work.
1.9
Research Objectives
The aim of this research is to determine the process equipment and support energy
in the semiconductor manufacturing process. The goal is to apply the research objectives to
all the energy intensive processes in semiconductor manufacturing.
16
A layering technique, “Oxidation process”, and a diffusion process – “Doping” in
semiconductor manufacturing are studied. A computer based interactive model in C ++
referred to as Semiconductor Energy Calculation Program (SECALPRO) is build to
estimate the energy consumed by these processes. The SECALPRO also determines the
support energy required for a specific operation. A system schematic is shown in Figure
1.11. The specific objectives of this research can be listed as follows:
1. Energy utilization and analysis in semiconductor manufacturing.
2. Sensitivity of production parameters with respect to energy consumption.
3. Verification and Validation of the designed SECALPRO model.
Input
Process
parameters
Output
Process Energy
+ Support energy
User
Model
No
Satisfied
Yes
Exit the System
Figure 1.11: System schematic
1.10 Conclusion
Increasing energy costs and dependability on foreign energy sources no further
allows neglecting energy conservation. The operating cost of a semiconductor fabrication
unit is a function of its energy requirement. Constant hike in energy costs and limited
availability of finance puts a never ending need of curtailment of the operating costs in the
production facilities. Process energy requirement must be analyzed in order to be
minimized. Energy awareness atmosphere alone doesn’t really help achieving energy use
optimization, but also needs energy quantification background along with it. It is wise to
say here, that energy or anything that matters can be saved only when it can be quantified,
and the factors affecting it can be defined. Also, replacement of physical energy
17
quantification with an equivalent reliable method is important. Hence, an interactive model
SECALPRO was built to determine the energy requirement of energy intensive processes
in semiconductor manufacturing. Based on the process parameters, the energy requirement
in semiconductor manufacturing could then be estimated, analyzed and studied for energy
conservation opportunities.
18
Chapter 2
2 Literature Review
2.1
General Energy Management Activities
Several energy management activities have been carried out in the U.S.
manufacturing sector. These involve initiatives taken to monitor process energy using
various control systems. Energy auditing also plays a major role in providing energy
management services. Several government funded research oriented organizations such as
Industrial Assessment Centre (IAC) perform these kinds of activities on a daily basis. Also,
private organizations are getting involved in most of the energy management and
conservation services.
Energy management through energy auditing activities is performed throughout the
Nation. A study by Packer [12] discusses about the energy audit performed at the Hewlett –
Packard Company (HP) Facility in Loveland, Colorado. The facility is a state of the art
involving research, wet process manufacturing, assembly and test, and surface mount
technology. Out of the total six buildings in the plant covering 1.1 million square feet area,
five buildings were audited. Important areas concerning energy such as utility management,
demand side management, developing an energy strategy, renegotiating gas costs were
implemented. Based on the recommendations from the energy audit from the regional IAC,
HP developed a utility monitoring and analysis system. It involved utility quality
monitoring, power management, integratability of monitoring systems, modeling of utility
flow, and data storage. It also collects and stores data such as kWh, amperage, power
factor, kW, and kVA. This information is further shared by several users and software
applications.
Energy conservation and management has applied Expert systems to develop
interactive software systems. Gopalakrishnan, Plummer, and Nagarajan [13] developed
computer
software
ENERGEX
capable
of
recommending
energy
conservation
opportunities (ECOs). It deals with opportunities in the area of lighting, boilers, motor
drives, and compressed air system. The software aids the user in arriving at appropriate
ECOs by presenting an interactive questionnaire. Based on the information input to the
19
system, corresponding ECOs are generated. It also provides an expert advice and
information on the generated ECOs and in general.
Dunning, Segee, and Allen [14] developed software for the self assessment of any
manufacturing facility. The software is capable of providing the common recommendations
provided by the U. S. Department of Energy’s, Industrial Assessment Program. Its
simplicity even enables a computer novice. It leads the user through an assessment of their
total facility. Once, the user inputs the necessary data, appropriate recommendations are
generated by the software. It is also an Expert system application successfully used as an
energy management tool.
Throughout the U.S., it is found that corporate practices regarding energy
management are uneven. Some facilities have strong energy management and conservation
policies while some are even unaware of it. Norland and Lind [15] made a study on
companies exhibiting best energy management practices. These companies do have a
strong commitment to reduce energy use and costs based on continuous energy monitoring.
One such company is the Owens Corning, manufacturers of fiberglass insulation products.
They have a three step approach on energy conservation and management: first, creative
procurement of energy resources, second, critical assessment and improvement in energy
quality, and last enhancement of energy efficiency. This approach is implemented by the
organization throughout. It resulted in annual savings of twelve million dollars for the year
1999.
Another example of corporate involvement in energy management activites would
be 3M [15]. This facility has been tracking its energy use since 1973. They have a target of
3 % reduction in energy usage per unit of output per year. Each 3M facility voluntarily
implements energy efficiency projects. They have a separate energy management
department which handles the energy usage data reported by these individual facilities.
Based on the quarterly reports, the management and application of energy conservation
policies becomes easy to implement. These activities are estimated to save approximately
2.7 million tons of carbon dioxide each year.
“As the field of energy management matures, so do the tools and best practices
available to ensure that the energy required by an organization is used in the most efficient
way possible. In the past, energy management practices consisted primarily of replacing
20
inefficient equipment and then using any number of methods to estimate the savings
gained. Studies performed by the Department of Energy (DOE) and the Texas State Energy
Conservation Office (SECO) have shown, however, that energy savings can be
dramatically increased and maintained over time by adopting and implementing consistent
energy management practices and recognized measurement and verification procedures. As
energy management standards and best practices begin to see widespread adoption, the
information systems required to support them will play a crucial role in their
implementation and success. These enterprise energy management (EEM) systems can
provide the detailed data and analysis capabilities required to ensure energy management
strategies and conservation measures are on track throughout an organization.
Organizations can apply EEM systems to gain a comprehensive understanding of current
energy performance, plan and select cost-effective energy conservation measures, track
performance of measures that have been implemented and verify the savings realized” [16].
Energy quantification on an individual equipment basis and also on a facility wide
energy monitoring system has been developed. Guidelines on energy quantification for
semiconductor manufacturing and equipment utilities are discussed. The semiconductor
equipment association of Japan (SEAJ) presented certain guidelines to calculate the energy
consumed by the semiconductor manufacturing equipment and in the clean room. The goal
of this study is to estimate the energy consumption of the plant utilities for the equipment
and items related to the equipment. These guidelines mostly deal with the use of power,
exhaust air, vacuum, dry air, cooling water, and ultra pure water by the equipment as well
as clean room energy [17].
The electric power measurement is carried out in three states viz. startup state,
processing state and idling state. An electric power meter and a voltmeter are used to
measure the equipment electric power in kW. The total process time is recorded for a
specific process. Processing energy per wafer can then be obtained by using the following
formula:
Processing Energy per wafer [kWh]
=
[kW] x [h]
N
Where;
[kW] : - Average Electric Power in kW’s
21
2.1
[h]
: - Wafer processing time in hours
N
: - Number of wafers processed during the time period
Energy Efficiency Survey for semiconductor manufacturing tools and equipment is
discussed. An Energy Analysis Survey (EAS) of a semiconductor plant in Santa Clara, CA
was done by XENERGY. The field study was conducted for the period between March 18,
1998 and May 12, 1998 by monitoring the load on the equipment [18]. Of the various
tools/equipment used in the facility, the XENERGY survey team monitored some of the
major equipments. A diffusion furnace was one of them. Diffusion furnaces are used for
chemical vapor deposition, which is one of the layering techniques. The facility had three
diffusion furnaces. The electrical service panel that distributes power to the diffusion
furnaces was monitored for the time period April 7th-April 16th. The data was monitored on
24 hour basis. The panel has three wires, three phase delta electrical configuration. The
current of each phase is measured and results show that the load is fairly balanced. The data
obtained from the study shows that the load is relatively constant the majority of the time.
Short duration spikes are most likely the result of inrush currents caused by the start up of a
furnace. The average load over the monitoring period was found to be close to the rated
current. The facility practices some energy efficiency activities to date. The important ones
are briefly listed further. The semiconductor plant in Santa Clara, CA has installed an
Energy Management System manufactured by Johnson Controls to monitor and control the
building systems. This system is set up to monitor critical data regarding the performance
and energy consumption of the building’s mechanical systems. Graphic representations of
building systems aid the facility staff in operating and controlling all of the support systems
in the building. Alarm points are set up to alert the building staff of equipment or outputs
needing immediate attention. This system enables trending of data to follow items of
interest over time and provide data needed for fine tuning various outputs. For example,
this feature is utilized on an ongoing basis to monitor temperature and humidity in the clean
room spaces and it was used to obtain useful data for this report on other system sensor
points.
Utility Consumption Characterization Protocol for Semiconductor Tools is an
article describing the methodology to determine power and energy requirements of
semiconductor tools. It addresses specific requirements for measurements and provides
22
electrical fundamentals for data interpretation and analysis. The system describes process
for monitoring the energy use by connecting the current and voltage probes to the control
panel of the equipment [19].
2.2
Productivity Improvement Activities in Electronics Sector
Product life cycles in electronics manufacturing are short. This in return demands
shorter design cycles [20]. Software tools can thus accelerate this design process to suit
short term product life cycles. The purpose of this study was to see how software tools help
in increasing the productivity of design process. The study also focuses on the risk
associated with the dependability on design software tools. A minute error on part of the
user might cause a complete rework.
Chatterjee’s [21] work on microelectronics industry stated that, the semiconductor
industry success will be governing the future of semiconductor industry. He mentions about
a productivity engine which cycles. The cycle starts from exponential trends in technology,
which will enable expansion of markets and revenues, which will further enable large
research and development. Continuous development in semiconductor devices helps keep
the cycle running.
Hong, Kim, Hang and Chae [22] in their article on “Throughput Analysis and
Productivity Enhancement for Chemical Vapor Deposition (CVD) Equipment”, stated that
the equipment efficiency and the productivity can be analyzed based on the history log. It
was found that there can be either a transfer bottleneck or a process bottleneck. Transfer
bottleneck could be nullified using productivity enhancement techniques, whereas process
bottleneck could be verified using process techniques.
2.3
Energy Efficiency Initiatives and Best Practices in Electronics Manufacturing
The Pacific Northwest Pollution Prevention Resource Center published a report
under the heading “Energy and Water Efficiency for Semiconductor Manufacturing” [9].
This report puts emphasis on improving the energy efficiency of the semiconductor
fabrication unit. Specific energy efficiency opportunities are presented to reduce energy
consumption and the associated energy costs. The opportunities are mainly categorized into
HVAC, Clean room, Process tools, and support utilities. The process tool opportunities
discuses about using high efficiency tool components such as motors, fans, pumps,
23
compressors. Efficient lighting systems result in high energy savings as well as reduction in
cooling load. Vacuum systems should be located as close to the application to reduce loss
of energy. Clean room energy conservation opportunities include, lower clean room make
up air flow rates, use of variable flow drives for recirculation and exhaust fan motors.
Major emphasis is put on reducing the clean room volume. Implementing these will reduce
the quantity of fresh and makeup air circulations, which in turn would, need lower clean
room energy requirements [9].
United States Environmental Protection Agency believes that moving of air to,
through and out of the clean room is the major concern for high energy consumption in
fabrication units. Reducing the amount of air flow or increasing the efficiency of the
equipments used for circulation will achieve significant savings. To verify the above
methodology, EPA modeled a hypothetical fabrication unit with a total square feet area of
16,000. The fabrication unit could manufacture wafers of size 125 mm. With reduction in
air velocity from 100 fpm to 90 fpm, a 10 % reduction resulted in a 27 % reduction in
power requirements [1].
Motorola has nine semiconductor fabrication units around the world implementing
significant energy conservation projects. Some of the major steps to achieve energy
conservation are discussed here [8].
•
Manufacturing tooling power consumption during idle times: Most of the
facilities do not operate for 100% utilization. Process tools like vacuum pumps in
turn consume more energy at the idling state. Significant energy savings can be
realized by turning off these equipments in idle conditions.
•
Clean room air velocity reduction: High efficiency particulate attenuation (HEPA)
filters are used in the plant clean rooms to maintain the specified class room
levels. It is observed from studies that reducing the air velocity from 90 fpm to 70
fpm does not have any effect on the clean room conditions, room pressurization,
and temperature and humidity set points. A substantial amount of energy is saved
alone by this implementation. Throughout its nine fabrication units, Motorola
achieved an average saving for $378,000 per year for a total clean room area of
380,000 square feet.
24
•
Other practices include optimizing chiller operating conditions, and exhaust
reductions: Water conservation energy savings are also one of the important areas
amongst those described earlier. A facility at Austin TX can save as much as 5.11
kWh of energy in order to pump 1,000 gallons of water. Most wafer fabrication
units use as much as 3 million gallons of ultra pure water per day. On an average
1,500 gallons of city water makes 1,000 gallons of ultra pure water [9].
At Toshiba a cogeneration system uses gas turbine with an output of 4,520 kW. The
cogeneration system operates from 8 in the morning till 10 in the night to reduce the
daytime load on the commercial power requirement. It also satisfies the steam requirements
of the plant. “Because the load reduction in the daytime raised the ratio of night-time power
use, the plant could make an adjustment contract based on the daily demand period. By
increasing the contract, demands in off-peak rate periods (22:00 - 8:00 on weekdays and all
day on Sundays and national holidays), and the plant now enjoys a very low-priced offtime rate by operating highly efficient turbo refrigerators in off-time.” The plant also
replaced motors with energy efficient ones and is now enjoying the benefits of lower
energy consumption [37].
2.4
Conclusion
The above literature describes energy management, quantification and conservation
in semiconductor industry and the measures taken towards reducing the energy cost in
semiconductor industry. It can be seen that significant research has been carried out in the
area of energy conservation in HVAC and clean room energy use. As stated earlier, process
tools consume a significant amount of process energy. There is an urgent need to identify
the production parameters which drive energy used for process tools. Literature is thin on
the determination of production parameters for a specific manufacturing process. Also, the
effect of these parameters on energy is yet to be analyzed. Developing a model such as in
any of the programming languages will give a better understanding of the use of process
energy as a whole without any interruptions in the production cycle. Also, models such as
these will help in sensitivity analysis of the parameters with respect to energy. Thus
research in this area will be of immense help to the semiconductor industry for analyzing
and improving on their energy efficiency.
25
Chapter 3
3 Research Approach
3.1
Methodology
The goal here is to estimate the energy used in semiconductor manufacturing. Not
all processes in semiconductor manufacturing are energy intensive. Referring to Figure 1.9
in Chapter 1 Introduction, it is found that layering and diffusion are the two energy
intensive processes in semiconductor manufacturing. Hence, it becomes necessary to
determine the manufacturing parameters for these two processes which are most sensitive
to energy.
Oxidation, a layering technique and doping, a diffusion technique are therefore
studied. Hence, an effort is made to apply the research approach to these processes. Before
going into detailed analysis in Chapter 4, Model Development, and Chapter 5, Results and
Sensitivity Analysis, relevant information regarding oxidation and doping processes is
described.
3.2
Oxidation/Layering
When a bare silicon surface is exposed to oxygen, the surface layer turns to silicon
dioxide. Silicon dioxide is a molecule having two atoms of oxygen and one atom of silicon.
Silicon is a semiconducting material and the silicon dioxide layer formed acts as a
dielectric (insulator). This combination of a semiconducting material with a dielectric
makes it one of the most commonly used layers for the semiconductor devices [23].
The important uses of silicon dioxide layer on a silicon surface are as follows [23]:
1 Surface passivation
2 Doping Barrier
3 Surface Dielectric
4 Device Dielectric
Surface passivation
Semiconductor devices are prone to contamination. Enough measures are taken to
limit the quantity of impurities in a clean room, but still there are minute particles left
which can cause the device to malfunction. Silicon dioxide layer serves this purpose of
26
protecting the silicon layer from contamination. It protects the surface in two ways viz.
physical protection and chemical protection.
The wafer surface is sensitive to dirt and other contaminants. Silicon dioxide also
known as glass is one of the hardest and non porous materials. As can be seen in Figure
3.1, silicon dioxide acts as a physical protection layer on the silicon surface. It also protects
the surface from scratches and wear, during fabrication processes. Some mobile ions or
electrically active elements end up on the silicon wafer surfaces. If left on the silicon
surface may interfere with the device functionality. Silicon dioxide layer grown on the
wafer surface uses silicon from the wafer itself. Oxide growing thus raises the mobile ions
to the silicon dioxide surface and restrains them from reaching the wafer surface. The
mobile ions are less harmful when brought to the silicon dioxide layer.
Silicon Dioxide
Layer
Silicon wafer
Figure 3.1: Oxide layer formation
Doping Barrier
Doping is the process that puts specific amounts of dopants through openings in the
silicon surface. Most of the dopants have a very low rate of movement in a silicon dioxide
layer as compared to the silicon. Hence only the exposed silicon wafer surface is doped as
shown in Figure 3.2.
Figure 3.2: Formation of doping barrier
Surface Dielectric
Dielectrics are also termed as insulators when used on an electrical circuit. The final
microchip or the end product has various layers of silicon dioxide with different circuitry at
each level. It is like a multi storied building. The silicon dioxide layer acts as an insulator in
between them. The oxide layer that covers most of the wafer surface is called a field oxide.
Figure 3.3 depicts the application of silicon dioxide layer as a surface dielectric.
27
Metal Layer
Silicon Dioxide Layer
Silicon wafer
Figure 3.3: Surface dielectric formation
Device Dielectric
The thin layers of silicon dioxide grown in the gate region are called gate oxides.
The gate is that part, which controls the operation of the device. It is used in Metal Oxide
Semiconductors (MOS) transistors. It is used to separate the metal from the semiconductor
and acts as the dielectric in the capacitor formed between metal and semiconductor.
3.3
Thermal oxidation process
The silicon dioxide layers are grown on the silicon surface by a method known as
thermal oxidation. The thermal oxidation process is carried out in the temperature range of
900º C to 1200º C. The atmosphere in the furnace where oxidation takes place can contain
either pure oxygen or water vapor. Oxygen arriving at the silicon surface then combines
with the silicon to form silicon dioxide.
Initially the reaction between oxygen and silicon at the surface is a surface reaction
only. After a specific thickness is build on the silicon surface, the oxygen molecules must
diffuse through the already grown silicon dioxide layer to reach and interact with the
silicon surface. The growth of silicon dioxide takes place in two stages viz. linear stage and
parabolic stage. The initial stage is called linear because the oxide growth is uniform for
each interval of time. After a thickness of about 500 angstroms or 0.05 microns is achieved,
a certain limit is imposed on the growth rate. This is due to the resistance or barrier
developed by the grown oxide. Either the silicon atoms or the oxygen atoms must move
across the silicon dioxide layer to further react. The oxygen diffuses through the oxide
layer to the silicon surface to form silicon dioxide. The source for the silicon is the wafer
itself. As the oxide layer is grown with time, the oxygen must overcome this growing
barrier to reach the wafer surface. The net effect is the slowing of oxide formation. This
stage is the second stage of oxide formation and is called as the parabolic stage. This
implies that, growing thicker oxides require much more time than to grow thinner oxides
[23]. A silicon wafer with silicon dioxide layer achieved during oxidation process is shown
in Figure 3.4.
28
Oxidizing agent (O2 or H2O)
Original Si Interface
Silicon Dioxide Layer
Silicon wafer
Figure 3.4: Oxide layer formation
The oxidation process can be categorized into two types viz.
1. Dry Oxidation
2. Wet Oxidation
The reaction of oxygen gas with the silicon at the wafer surface results in silicon dioxide.
This simple reaction can be written as;
Si (solid) + O2 (Gas)
SiO2 (solid)
(3.1)
Water vapor at high temperatures used in the oxidation process is in the form of steam, and
this process is called as wet oxidation, steam oxidation, or pyrogenic oxidation.
Si (solid) + 2H2O (Gas)
SiO2 (solid) + 2H2 (gas)
(3.2)
In the dry oxidation process, thicker oxides require much more time than in wet
oxidation process. In wet oxidation, the vapor or the oxidizing element has one hydrogen
atom and one molecule of oxygen and hydrogen with the negative charge. This molecule
called as the hydroxyl ion has higher diffusivity in the silicon dioxide layer grown to that of
oxygen. This results in faster oxidation of the silicon.
3.4
Oxidation process parameters sensitive to energy
As discussed in the previous section 3.3, Thermal oxidation process, it was
mentioned that, the layering process is carried out in a furnace at elevated temperature.
Oxidation growth rate depends on the following parameters. These parameters alter the
linear and parabolic growth rates. This result in variations in oxidation time required to
grow the desired oxide film. Energy as a function of process time also varies with the
change in process time [24]. The process time and hence the energy usage will therefore be
altered by varying one or more of the manufacturing parameters listed below. The
parameters are:
1. Oxidation temperature
29
2. Oxidizing medium
3. Crystal orientation of silicon usually <100>
4. Silicon doping level
5. Oxidation pressure
Oxidation temperature
The linear and parabolic growth rates are directly proportional to the oxidation
temperature. At elevated temperatures the linear and parabolic growth rates are higher
which minimizes the oxidation time required. Hence at elevated temperature, the process
can be accelerated to reduce the process time, which will reduce the energy used for
growing the oxide film. But at the same time, energy used to maintain this high temperature
for the operation will increase the furnace energy consumption. Therefore, the effect of
varying temperature on energy utilization needs to be analyzed.
Oxidizing medium
The oxidizing medium or agent is one of the key factors which alter the rate of
oxidation. Oxidation rates are much higher in the case of wet oxidation as compared to dry
oxidation. The hydroxyl ions in the wet (water vapor) oxidation have higher diffusivity
than oxygen in the silicon dioxide layer grown. This characteristic helps in higher diffusion
rates through the oxide layers already produced. At any given temperature, wet oxidation
accelerates the growth process. Hence, at a specific process temperature, the furnace power
requirement for wet and dry oxidation will be the same, but the energy usage in kWh will
be less for the former because of the reduced process time.
Crystal orientation
The oxidation growth rate depends on the number of silicon atoms on a crystal
plane. Figure 3.5 shows the schematic of two different types of wafer orientation, <100>
and <111>. In case of <111> orientation, the number of silicon atoms are more than in
<100> plane. Hence more silicon atoms react with the oxidizing medium to accelerate the
formation of silicon dioxide [25]. Hence, the energy used for <111> and <100> orientation
will vary. But, it may not be always true. For a combination of different process
30
temperatures and oxidizing medium, this relationship may not hold true and therefore needs
to be analyzed further in detail.
<111>
<100>
Figure 3.5: Crystal orientation of silicon wafer [25]
Silicon doping level
Most commonly used doping elements in semiconductor manufacturing are boron,
phosphorus, arsenic and antimony. The choice of dopant depends on the N or P type gates
to be formed on the surface. When doped with boron, it weakens the SiO2 layer. This
weakening of the already developed oxide layer allows faster penetration of O2 or H2O in
case of dry and wet oxidation respectively. Phosphorus does not have this property of
weakening the silicon and oxide bond at the interface. It only segregates at the elevated
temperature.
Oxidation Pressure
Increase in oxidation pressure results in higher oxidation rates because the linear
and parabolic growth rates are directly proportional to the partial pressure of the oxidizing
species. When it is desired to have shallow diffusions and thin oxide layers, it becomes
necessary to use low temperature ranges. In order to maintain the oxidation time, high
pressure oxidizing element is made to pass through the furnace tubes at a maximum
pressure of 25 atmospheres. “A constant growth rate can be maintained, if for each increase
in pressure of 1 atmosphere, the temperature is decreased 30 degrees Celsius” [24].
The oxide layer developed at higher pressure has certain disadvantages. There is
less uniformity in oxide thickness developed because of temperature inhomogenities
developed by high pressure streams. The equipment is less safe because of high operating
pressure gases. Also, the equipment needs a lot of floor space. These factors restrict the use
of high pressure oxidation.
31
From the above discussion, it is thus evident that energy used in oxidation process
is a function of the process time. Further, the process time is a function of the process
temperature, oxidizing medium, and the crystal orientation. <100> orientation is usually
used for semiconductor devices. Hence, it becomes futile to study the <100> vs. <111>
effect on energy requirement. The remaining two parameters i.e. doping level and the
process pressure are less feasible and hence not considered for energy analysis. A
mathematical model used to determine the process time is described further.
3.5
Deal Grove Model
A mathematical model to showcase the relation between the above mentioned
manufacturing parameters and the process time is presented. Bruce Deal and Andy Grove
[26] developed such a model to determine the oxidation process time. The equations
described in this model are used in the SECALPRO model to determine the oxidation
process time. The Deal Grove model is presented here [26];
The reaction occurs at the silicon and the oxide interface. The process dynamics is
shown in Figure 3.6. The oxide molecules have a higher diffusivity than the silicon in the
oxide layer formed. The reaction is affected by the following factors:
1. The concentration of oxidizing element molecules (O2 or H2O) referred to as No
2. The flux of the diffusing molecules J (cm-2. Sec-1)
3. The concentration of oxidizing molecules at the interface Ni
SiO2
O2 or H2O
Silicon Wafer
Ni
tox
No
Figure 3.6: Oxide growth dynamics [26]
32
Deal and Grove found that the rate at which oxide grows depends on the speed of oxidizing
element to diffuse through the oxide layer, and the rate of reaction at the interface [26]. It
was assumed that the flux of oxidizing elements reaching the interface is equal to the flux
consumed in the reaction.
JSiO2 = J i
(3.3)
According to Fick’s law the flux of the diffusing element or material depends on the
concentration gradient of the material
J = −D
dN
dx
(3.4)
D (cm2/sec) is the diffusion coefficient also called as diffusivity is temperature dependent.
It shows an Arrhenius type relationship [26]
⎛ − Ea ⎞
D(T ) = Do Exp⎜
⎟
⎝ kT ⎠
(3.5)
Where Ea is the activation energy required for the reaction to occur and k is the gas
constant.
Further it was assumed that,
J = −D
(N o − Ni )
t ox
(3.6)
Also the reaction rate ks (cm/sec), depends on the concentration of oxidizing elements at the
interface which in turn depends on the flux of the molecules at the interface, this can be
stated as,
J = ki N i
(3.7)
⎛ − Ea ⎞
k s (T ) = k so Exp⎜
⎟
⎝ kT ⎠
(3.8)
Where,
The Growth Rate of the SiO2 is defined as the rate at which the thickness changes with
respect to time.
33
GR =
dt ox
dt
(3.9)
The Growth Rate depends on how fast the silicon atoms from the wafer surface react with
the oxidizing element, which in turn depends on the flux of the elements at the interface.
GRαJ i
(3.10)
The proportionality constant is the number of density (M in cm-3) of the oxygen atoms in
the SiO2 layer.
GR =
Ji ks Ni
=
M
M
(3.11)
The concentration of oxidizing molecules at the interface Ni can be found from the above
equations to be,
Ni =
DN o
ks
×
ks
k s × t ox + D
(3.12)
After substituting the value of Ni in the equation 3.11,
GR =
DN o
ks
×
M
k s × t ox + D
(3.13)
As stated earlier, the growth rate is linear for small oxide thickness and is parabolic for
larger thickness. For small thickness, tox is far less than A/2 and can be neglected; hence the
growth rate will be,
GR =
ks No
M
(3.14)
For larger thickness, tox is far greater than A/2, hence the growth rate will be,
GR =
B
2t ox
The above equation can be further reduced to,
34
(3.15)
GR =
B
2
×
2 2t ox + A
(3.16)
B=
2 DN o
M
(3.17)
A=
2D
ks
(3.18)
Where, A and B are the constants. Such that B/A is the linear growth rate and B is the
parabolic Growth rate of the oxide. After integrating the equation for tox with respect to
time, the following equation 3.19 is derived. The oxidation limits would be from time zero
(to) at which the oxidation starts to (tox) at which the oxidation is completed.
2
t
t
Time(hrs) = ox + ox − τ
B
B/ A
(3.19)
Where τ is the time (hrs) required for the oxidation of any previously grown oxide layer
present on the Silicon wafer [26]. The above formula is used in the SECALPRO model
described in Chapter 4, Model Development, for the estimation of time required to grow
oxide layer on silicon wafer surface. This formula takes care of the linear and parabolic
stages of the oxide growth.
3.6
Doping
Doping can be defined as the process in which a specific number of dopant atoms
are introduced into the wafer surface through the openings. Doping can be carried out by
two methods, thermal diffusion and ion implantation. Thermal diffusion is a chemical
process and ion implantation is a more of physical process. Diffusion is carried in the range
of 900 – 1200 °C whereas in ion implantation, the dopant atoms are ionized, accelerated
and further shot onto the wafer surface. At the time of ingot formation from the raw silicon,
the melt is doped with either a P type or N type dopant. Doping is carried out to form p and
n junctions across the wafer in order to form pn junctions or pnp transistors. Diffusion
process involves high temperature operation and uses a diffusion furnace similar to that
necessary for oxidation process. On the other hand, ion implantation involves the use of
equipment similar to a shot gun, which is comparatively very less energy intensive. [27]
35
3.7
Predeposition
Predeposition is a process of introducing the dopant atoms at a higher concentration
to shallow depths. It is carried out in lower temperature vicinity of 900 °C and for a very
short period of time. Deposition is a function of the “diffusivity” of dopant material into the
silicon wafer. The higher the diffusivity, the faster is the movement of dopant atoms into
the wafer.
Dopant atoms
O2 or Water Vapor
Oxide barrier
Figure 3.7: Predeposition process
Figure 3.7 shows the pre-deposition process in which the dopant atoms are introduced into
the wafer surface to shallow depths in the presence of O2 or Water Vapor. Another
important factor which controls predeposition is the “maximum solid solubility” of the
dopant atoms into the wafer. The “number of atoms” introduced into the wafer surface is
deposition time dependent.
3.8
Drive-in oxidation
Drive-in or drive-in oxidation, the next step after the predeposition step is shown in
Figure 3.8. Here, there is no external dopant source. Dpoant atoms introduced during the
predeposition step are driven deep into the wafer by the application of heat. It is carried out
at a high temperature range of 1,050 – 1,200 °C [27]. The surface concentration reduces in
drive-in process but the number of atoms already introduced into the wafer is constant. The
distribution of dopant atoms here is different than the error function distribution obtained in
the predeposition process. It is named as Gaussian distribution. Simultaneously, the
exposed silicon surface of the wafer is oxidized by the presence of oxygen or water vapor
in the furnace tube.
36
New oxide layer
Figure 3.8: Drive-in process
As discussed earlier, diffusion technique is driven by the process temperature.
Parameters such as diffusivity, maximum solid solubility, junction depth, and the number
of dopant atoms can be increased at the expense of temperature. Therefore, increasing the
process temperature will reduce the process time and energy requirement of the furnace to
an extent, but at the same time realizing higher temperatures will lead to additional energy
consumption. For this reason, the effect of the parameters mentioned above on energy
requirement is studied in Chapter 5, Results and Sensitivity Analysis.
3.9
Dopant sources
The common dopant sources available are in the form of solid, liquid and gaseous
state. Antimony, arsenic, and phosphorus are the commonly used N type dopants. Boron is
the typical P type dopant used. Precise control through regulation is obtained by using
dopant sources in gaseous form. Also, these sources are comparatively cleaner because the
gases are stored in pressurized cylinders.
3.10 Diffusion process parameters sensitive to energy
1. Junction depth
2. Diffusion temperature
3. Diffusivity
4. Maximum solid solubility
5. Number of dopant atoms
Junction depth
The depth into the wafer surface at which the junction is formed is termed as a
“junction depth”. Larger junction depth is achieved at the expense of long drive in process
time. Processing of wafers for a longer period of time in equipment such as a diffusion
37
furnace leads to higher energy usage. Process time and thus energy utilization could be
reduced by increasing the drive in process temperature.
Diffusion temperature
Diffusion temperature is the temperature at which the dopant atoms are diffused
into the wafer surface. This temperature differs for the predeposition and drive in steps. Pre
deposition process temperatures lie in the vicinity of 900 °C, whereas drive in process is
carried out at elevated temperature range above 1000 °C. Power consumed by the diffusion
furnace is related to the process temperature. Increasing process temperature will definitely
accelerate the process and reduce the process energy consumption. But, there will be some
additional energy required to achieve the high temperatures. Hence, an analysis is done in
Chapter 5, Results and Sensitivity Analysis, to study the effect of varying process
temperature on energy.
Diffusivity
It can be defined as the rate at which the dopant atoms move into the wafer surface.
Higher diffusivity is desired. Diffusivity is a function of process temperature and increases
with the increase in temperature. Hence, increasing process temperature will accelerate the
diffusion process. It is also a function of dopant atoms.
Maximum solid solubility
It is the maximum number of atoms that can be introduced into the wafer surface at
the specified temperature. It increases with increase in temperature.
Number of dopant atoms
The higher the number of dopant atoms, the deeper the junction depth achieved or
the heavier the silicon is doped. It is because in order to form a junction, the number of
dopant atoms should equal the concentration of the host atoms in the wafer. If the number
of dopant atoms introduced in the wafer during the pre deposition step is less, then less
number of dopant atoms need to be driven deep further in the wafer in the drive in step, and
hence the junction depth achieved will be less. Deposition is carried out in two steps or
consists of two processes, pre deposition and drive in.
38
3.11 Diffusion model
A diffusion model which determines the effect of process temperature on
diffusivity, surface concentration of dopant atoms, number of dopant atoms required for
diffusion, and finally the junction depth achieved was developed by Fick [28]. Formulae
presented in the following text are used in the SECALPRO model to estimate the process
time and energy requirement of a diffusion process.
Fick stated that if the dopant or impurity concentration gradient exists in a
substance, there is a common tendency of the dopant to move into the substance until the
gradient is reduced and the dopant concentration is uniform throughout the substance. This
effect is called as diffusion [28]. According to Fick’s law, the flux of the diffusing element
or material depends on the concentration gradient of the material
F=
− D × dC ( x, t )
dx
(3.20)
D (cm2/sec) is the diffusion coefficient also called as diffusivity is temperature dependent.
It shows an Arrhenius type relationship [28].
D (T ) = Do Exp
− Ea
kT
(3.21)
Although Fick’s first law is important, it does not tell about the change in dopant
concentration in a substance with time. Fick’s second law addresses this issue.
dC ( x, t ) − dF
=
dt
dx
(3.22)
By substituting F from equation 3.20 in equation 3.22, Fick’s second law in one dimension
is obtained.
dC ( x, t )
dC ( x, t ) d
=
D×
dx
dt
dx
(3.23)
Figure 3.9 shows the relation between dopant concentration and junction depth for varying
pre deposition time t1, t2, and t3.
39
Dopant concentration
Cx
C sub
t1
t2
t3
Depth
Figure 3.9: Dopant concentration vs. junction depth – Error function curve
Diffusion coefficient D is independent of the position and hence can be taken out from the
differential and can be written in a simpler form as follows:
dC ( x, t )
d 2 C ( x, t )
= D×
dt
dx 2
(3.24)
For low dopant concentrations, the above assumption is correct, but for higher
concentrations, the second law equation 3.24 must be solved further. The diffusion is
carried out in two steps; predeposition or constant supply deposition and drive-in step or
fixed dose process. The mathematics of predeposition is given by the following equations;
C ( x, t ) = 0
(3.25)
Two boundary conditions for this step would be;
C (0, t ) = C s
C (∞, t ) = 0
(3.26)
(3.27)
The solution to equation 3.24 for the above two conditions is given by;
C ( x, t ) = C s × erfc(
40
x
( Dt )
)
(3.28)
Where, erfc is known as complementary error function. The mathematics of pre drive-in
process is given by the following equations;
C ( x, t ) =
Qo
− x2
× Exp(
)
(4 Dt )
ΠDt
(3.29)
Where, Q0 is the fixed dopant dose and is given by the following;
Qo = 2C s ( Dt / Π
(3.30)
Equating equation (3.29) to the substrate concentration Csub will yield the desired junction
depth x . Figure 3.10* show the junction depth achieved in pre-deposition and drive in
Dopant concentration
process.
Cx
Pre deposition - Error
function curve
Drive in – Gaussian
C sub
Depth
Figure 3.10: Dopant concentration vs. junction depth – Gaussian curve
* Figure not to scale
3.12 Conclusion
This chapter deals mostly with the first research objective and in part the second
also. Initially, the energy intensive processes (Layering – Oxidation and Diffusion –
Doping) in semiconductor manufacturing are outlined. Later, the manufacturing parameters
most sensitive to energy are listed, pertaining to these energy intensive processes. A brief
41
discussion on the effect of manufacturing parameters with respect to energy is done. Also,
the components of oxidation and diffusion dynamics are discussed, which are further used
in Chapter 4, Model Development and Chapter 5, Results and Sensitivity analysis.
It is further revealed that, varying the manufacturing parameters have an impact on
the process energy requirement. In order to study their effect, it is first necessary to
estimate the process energy for a set of manufacturing parameters and then perform a
sensitivity analysis to study their individual effect on energy.
42
Chapter 4
4 Model Development
4.1
Purpose of Modeling
This chapter deals with the development of a computer based model referred to as
Semiconductor Energy Calculation Program (SECALPRO), to estimate energy requirement
in semiconductor manufacturing. The objective here is to exactly model the manufacturing
process carried out in a clean room. The following are some of the major purposes of
building a SECALPRO model:
1. Estimate process energy requirement for energy intensive processes.
2. Estimate support energy requirement for the selected process.
3. Facilitate the user to study the effect of process parameters on energy by
running the SECALPRO model for varying process parameters.
4. Export the results of the analysis to an Excel® spreadsheet.
5. Graphical representation of the results obtained from the analysis.
4.2
C ++, SECALPRO Model Programming Language
“C programming language has been widely accepted for all applications, and is
perhaps the most powerful of structured programming languages. In recent times, object
oriented programming has become popular. Now, C ++ has the status of a structured
programming language with object oriented programming methodology, in which the
software reusability, testability, maintainability, portability, and reliability are the key
features and requisites of modern software development. Initially C ++ was not considered
any different than C language but only an addition of few features. But, this misconception
is widely ruled out by the recognition of C ++ language as well designed and object
oriented programming language” [29].
4.3
SECALPRO Model Assumptions
The following assumptions are made while designing and running the SECALPRO
model.
1.
All the four tubes in a horizontal stack tube furnace are used for one operation at
any given time.
43
2.
The utilization factor for support equipment like wafer cleaner, and wafer scrubber
is dependent on the furnace throughput per month.
3.
The power factor is assumed to be 90%, as the facility will have significant
induction heaters and motors.
4.
The furnace will cycle continuously on a 24 x 7 x 4 basis. A total of 4 weeks per
year is assumed to be the maintenance, breakdown and shutdown time.
5.
Separate Air handler units, Makeup fan units, and Fan filter units for Clean room
Class maintenance are considered based on the clean room Class and space
requirements. Facilities might have individual units for a specific clean room Class
and section or a centralized chiller. Both types of practices are carried out in
facilities.
6.
The idle temperature of the furnace is considered as 750 °C. Industrial furnaces idle
in the 600 °C - 800 °C temperature range.
7.
The ramp down is controlled; it means the furnace is not shut off while cooling
down and consumes power corresponding to 750 °C temperature.
8.
Throughput per month implies the number of wafers that will visit the furnace once
in a month for a given process. A month is considered to be of 4 weeks on an
average.
4.4
SECALPRO Model Development
Model development starts with outlining the process flow for the energy intensive
processes. A step by step explanation of the individual components of the SECALPRO
model is given.
4.4.1
Layering/Oxidation Process flowchart
Figure 4.1 describes the oxidation process carried out in a clean room. Wafers that
need to be oxidized are first cleaned to remove any foreign particles such as dust, dirt,
abrasion caused due to earlier processes and wafer handling. The contamination free wafers
are then cleaned by using a scrubber to obtain smooth polished surface. The wafers are then
ready to be oxidized in the oxidation furnace. Movement of wafers within the clean room is
accomplished, in part manually and in part with power arms. Oxidized wafers are inspected
for its various properties. Here inspection failure refers to under grown oxide layers and
44
uneven oxidation. Based on the severity of the defects, wafers are allowed to pass through
the process. Good wafers are ready for the photolithography process.
Input
Wafers
Wafer Handling
Wafer
Cleaning
(Rinsing,
drying)
Wafer Handling
Wafer
Layering/Oxidation
Wafer
Scrubbing
Wafer Handling
Inspection
Fail
Pass
Exit the System
Figure 4.1: Oxidation/Layering process flowchart
4.4.2
Diffusion/Doping Process flowchart
Figure 4.2 describes the diffusion/doping process. Wafers that need to be doped are
required to pass through the pre-clean process to remove particulates and stains similar to
those necessary for oxidation process. The wafers should be etched after the pre-clean
process, because oxide is grown on the exposed silicon wafer surface using chemicals in
the pre-clean. Etching involves immersion of wafers into a HF solution and a rinse and dry
step. Also, small amount of oxide is grown on the wafer surface due to exposure to the
surrounding air. Etching removes this unwanted layer of oxide to allow unimpeded doping
activity. Care should be taken to prevent excessive removal of surface blocking oxide.
Wafer scrubber is used to remove particles that cannot be removed by the pre-clean
process. The wafer is then ready for predeposition process using a diffusion furnace, where
higher concentration of dopant atoms is introduced into the wafer to shallow depths.
Etching is again required using HF solution to remove any thin oxide formed during
predeposition which can act as an unwanted dopant source in the drive-in oxidation step.
Drive - in oxidation is also carried out in a diffusion furnace.
45
Input
Wafers
Pre - Clean
& Etch
Wafer
Scrubbing
Etch
Drive - in
Oxidation
Pre
Deposition
Etch/
Deglaze
Inspection
Fail
Pass
Exit the System
Figure 4.2: Diffusion/Doping process flowchart
4.5
Areas of significant energy consumption
Referring to the Figures 4.1 and 4.2, the dotted areas represent candidates for
significant energy consumption and are listed below:
1. Wafer cleaning
2. Wafer oxidation/diffusion
3. Clean room class maintenance
Wafer cleaning is accomplished by using a wafer cleaner. The wafer cleaner has
heating elements to maintain hot bath temperature, a washer to rinse the wafers, and a drier
to dry the wafers before they are oxidized/diffused. All of these individual elements in a
cleaner consume significant amount of energy. As described earlier the wafer
oxidizer/diffuser/furnace is process equipment where the oxidation/diffusion is carried out
at elevated temperatures. Detailed elements of the system are discussed in section 4.7.3.
Maintaining clean room class levels also demand significant amount of energy. Analysis on
the following pages reveals the high end use of energy by these elements.
4.6
Systematic approach to estimate energy consumption
A systematic approach to estimate energy consumption in Layering/Oxidation and
Diffusion/Doping process is shown in flowchart in Figure 4.3.
46
Select the desired energy intensive process
1.
2.
Layering – Oxidation
Diffusion – Doping
Enter Dopant type
Enter Oxide thickness
desired in microns
Enter Predeposition
time in hours
N
If Oxide
thickness <
500 A
Wet Oxidation
SiO2 + 2H2
Si + 2H2O
Enter Predeposition process
temperature in °C
Y
Enter Drive-in process
time in hours
Dry Oxidation
Si + O2
SiO2
Enter Drive-in process
temperature in °C
Enter Wafer Orientation
N
If <1OO>
Orientation
Y
Enter Substrate concentration in
atoms/cubic centimeter
Specify
<111> Dry
Or
<111> Wet
Determine number of dopant atoms
introduced during Predeposition step
Specify <1OO> Dry or <1OO> Wet
Determine Junction depth
in microns
Enter Oxidation process
Temperature
Determine time required to grow
the specified oxide layer
Enter Wafer Diameter/Size
Continued
Figure 4.3: Flowchart to determine energy consumption
47
Continued
Determine Throughput (Wafers
processed/month)
Determine process Cycles per
month
Enter idle temperature of the
Furnace
Specify the Ramp up rate desired
in °C/minute
Determine Total Ramp Up
time/Cycle in hours
Determine Total Ramp Down
time/Cycle in hours
Determine Total Process
time/Cycle in hours
Determine Total Ramp up Power &
Energy consumed/Cycle by Furnace in
kW & kWh
Determine Power & Energy
consumed/Cycle by Furnace in kW & kWh
for Process (Oxidation/Doping)
Continued
Figure 4.3: Flowchart to determine energy consumption
(Continued)
48
Continued
Determine Total Ramp down Power
& Energy consumed/Cycle by
Furnace in kW & kWh
Determine Total Power & Energy
consumed/Cycle by Furnace in
kW & kWh
Determine Furnace Controller and Base
level/Idle operation Power & Energy
consumed/Cycle in kW & kWh
Determine Support Equipment Power and Energy
consumed/Cycle
Determine Wafer Handler motor Horse power, and
Power & Energy consumed/Cycle in kW & kWh
Enter kVA Rating of Wafer Cleaner
Determine Wafer Cleaner Utilization and kW and
kWh Usage/Cycle
Enter kVA Rating of Wafer Scrubber
Determine Wafer Scrubber kW and kWh Usage/Cycle
Determine Total Support Equipment kW and
kWh Usage/Cycle
Enter Clean Room 1 and 2 Class desired and volume in
cu. ft
Continued
Figure 4.3: Flowchart to determine energy consumption (Continued)
49
Continued
Determine the number of Fan Filter Units
(FFU) required for Clean Room
Determine Fan Filter Units (FFU) Power &
Energy consumed/Cycle in kW & kWh
Enter the Clean Room ceiling area in
square feet
Determine Power and Energy consumed by
Clean Room Air handler unit, Air handler
Fans, and Makeup Fans in kW and kWh
Determine Clean Room Cooling Load due to
Equipment, Operators, and FFU in kW and kWh
Determine Total kW and kWh consumption per month
Determine Total kW and kWh consumption per Wafer
Figure 4.3: Flowchart to determine energy consumption (Continued)
4.7
Flowchart Description
Step by step explanation of the flowchart elements is given further. The user needs
to input information regarding the process.
4.7.1
Process selection
The user is given a choice to select the desired energy intensive process to
determine the process energy and also the support energy usage involved. As stated earlier,
in semiconductor manufacturing, Layering and Diffusion are the two highly energy
intensive processes. A common and most important aspect about these processes is that
both of them require use of high temperature diffusion furnaces and also significant energy
use for support equipment. Almost all activities are carried in clean room and their Class
varies depending on the extent of the cleanliness required.
50
4.7.2
Input variables/ Process related
The following variables are pertaining to the process. Selection of the process
variables totally depends on the functional requirement of the product. These variables also
determine the energy requirement for the selected process. Process variables for oxidation
and doping process are described further.
Layering/Oxidation
The following variables used in the SECALPRO model are important from oxidation
process point of view.
Silicon dioxide thickness
Silicon dioxide thickness varies depending on the purpose for which it is to be
grown on the wafer surface. For gate oxides, the desired thickness ranges from 50 A to 500
A, where as for field oxides the thickness is as large as 10,000 A [3].
Oxidation Type
The next step is to determine the type of oxidation required for growing the desired
silicon dioxide layer. Dry oxidation is used for growing thin oxide layers up to 500 A. If
the desired thickness is greater than 500 A, then wafers are oxidized by wet oxidation
process.
Orientation Type
<111> orientation (planes) has more silicon atoms as compared to <100>
orientation. Selecting the former will result in faster oxide growth. Present day
semiconductor devices mostly process <100> orientation. The following Table 4.1 lists the
values for growth rates and activation energy used to determine the time required to grow
oxide layers on a bare silicon surface. As can be seen in Table 4.1, the diffusivity Go for
<100> orientation, linear growth rate B/A, and wet oxidation is 9.7E+07 µm2/hr as
compared to 3.7E+06 µm2/hr for dry oxidation. Hence, it is clear that wet oxidation will
accelerate the diffusion process.
51
B/A
B
Wet
Go
9.70E+07
386
Ea
2.05
0.78
Dry
Go
3.70E+06
772
Ea
2
1.23
B/A
B
1.63E+08
386
2.05
0.78
6.23E+06
772
2
1.23
Orientation
Growth rate
<100>
<111>
Table 4.1: Growth rates and activation energy [26]
Oxide growth time
As stated earlier time required growing oxide is initially linear and tends to be
parabolic as the level of grown oxide increases. The linear and parabolic growth rates [26]
are given by the formula 4.1;
G = Go x Exp (- Ea / KT)
(4.1)
Where,
G = Parabolic growth rate (B) or Linear growth rate (B/A)
Go = Diffusivity (µm2/hr)
Ea = Activation energy which carries out the oxidation in electron volt (eV)
T = Oxidation process temperature in Degree Kelvin
K = constant
2
t
t
Time(hrs ) = ox + ox − τ
B
B
A
(4.2)
Where,
tox = Oxide thickness (microns)
τ = Time required to grow the present oxide layer (hrs)
Here, the term τ is zero because the wafers are assumed to be oxide free i.e. no layer of
oxide is present on the wafer surface. The linear and parabolic growth rates are estimated
using equation 4.1. The SECALPRO model estimates the time required to grow the oxide
layer using equation 4.2.
52
Oxidation Process Temperature
The oxidation process can also occur at room temperature. But in order to achieve
quality oxide layers and to enhance the rate of oxidation; the process is carried out at
elevated temperatures. Oxidation Process Temperature ranges between 900 ºC to 1200 ºC
[3].
Diffusion/Doping
The following variables used in the SECALPRO model are important from doping process
point of view.
Dopant type
The user can use any one of the P type or N type dopant and is asked to specify the
necessary dopant. The SECALPRO model selects Boron as P type dopant and Phosphorus
as N type dopant [30].
Predeposition time
Next, the user needs to specify the time period for which the predeposition of
dopants will occur. Usually, the time required for predeposition process is very short
because this process introduces the dopant atoms to a shallow depth less than 0.05 microns
[30].
Predeposition temperature
Also, the predeposition process temperature is low. Diffusion is dependent on the
temperature at which it is carried out and for the given time. Hence, varying the
temperature and time will result in a plot close to error function curve as explained in
Chapter 3 Research Approach. The user is asked to specify the desired predeposition
temperature in degree Celsius [30].
Drive-in time
Drive-in time is comparatively longer than predeposition time and may last as long
as 5 hours and above for typical junction depths of 2 – 3 microns. The drive-in time is long
because the dopant atoms need to travel long distance into the wafer surface to achieve the
desired junction depth [28].
53
Drive-in Temperature
Higher temperatures are necessary to drive the huge concentration of dopants deep
into the wafer. The user is asked to input the desired drive-in temperature in degree Celsius.
Number of dopant atoms
The higher the number of dopant atoms, the deeper the junction depth achieved. It
is because in order to form a junction, the number of dopant atoms should equal the
concentration of the host atoms in the wafer. If the number of dopant atoms introduced in
the wafer during the predeposition step is less, then less number of dopant atoms need to be
driven deep further in the wafer in the drive-in step, and hence the junction depth achieved
will be less using equation 3.29 from Chapter 3 Research Approach. Also, the number of
dopant atoms introduced into the wafer during predepostion step can be found using
equation 3.30.
4.7.3
Horizontal Tube Furnace
The schematic of a horizontal furnace tube and heating coils used for
oxidation/diffusion of silicon wafers is shown in Figure 4.4. It consists of a horizontal tube
made of mulite. The inside surface of the mulite tube has coil tubing. There are generally 3
to 5 tubes, which represent the heating zones of the furnace. Each heating coil has a
separate power supply for different operating temperatures. The reaction chamber is made
of a quartz tube called as quartz reaction tube, where the oxidation/diffusion process is
carried out. This tube is inside the mulite tube.
Separate thermocouples are positioned in each zone of the quartz tube to send
temperature readings to individual band controllers of the coils. These controllers are
linked with the power supply and provide power to the coils as per the desired temperature
[23].
System Components
The furnace system has the following main components or sections:
54
Figure 4.4: Schematic of furnace tube and heating coils [23]
Reaction chamber or Furnace section
Reaction chamber is a tube made of quartz where the oxidation of silicon wafer
occurs. One end of the reaction tube is connected to the source cabinet and the other end is
connected to the load station. Source cabinet is a source or inlet for gas into the reaction
tube. The wafers are loaded into the tube through the load station end. Quartz is mostly
used as the tube material, known for its high purity and stability level at elevated
temperatures range 1200º C and above. The production tube furnaces have more than one
tubes stacked vertically. This system of multi tubes is called as a furnace section as against
a single tube reaction chamber.
Temperature control system
It consists of thermocouples encircling the reaction tube along its length. They are
connected to the individual band controllers that feed the power to the heating coils. These
band controllers maintain even temperatures in the heating zones and control the power to
the coils. They trigger the power supply as and when the tube zone temperature is different
from the desired level.
55
To avoid warping of the wafers, a method known as ramping is employed. It means
the furnace entrance section is maintained at a temperature much lower than the actual
process temperature. The temperature increases towards the flat zone where the oxidation
of silicon wafer occurs.
Source cabinet
Individual tubes require a set of gases to accomplish the desired chemical reaction.
Three main gases are required for the oxidation process; they are oxygen for dry oxidation,
water vapor for wet oxidation and nitrogen. Dopant sources are located in the source
section of the furnace. Nitrogen is essentially required in the loading and the unloading
stages. Hence, the loading and unloading processes are done in an inert atmosphere. It is
continuously flown into the tube when the furnace is idle. It helps keep the dirt out of the
system.
The gases should be delivered to individual tubes in a specified sequence, pressure,
flow rate, and time. The equipment which takes care of these individual tube requirements
is the source cabinet. The inlet of tubes is connected to the source cabinet which is a system
containing pressure gauges, flow meters, filters, and timers. This is referred to as a gas
controller. A bubbler is used when the system requires a mixture of gases.
Wafer cleaning station
Wafers undergo cleaning before entering the furnace because contamination left on
the surface may diffuse into the wafer surface. The wafers are cleaned in wet bench with
chemicals, rinsed in deionized water and dried.
Wafer load station
Wafers once cleaned are loaded onto the boats or cassettes made of quartz or silicon
carbide. These boats are then loaded into the furnace tube. A quick wafer surface
inspection for contamination is performed under high frequency ultraviolet lights, which
takes care of particles left undetected by the naked eye. The wafer boat or cassette is loaded
automatically into the furnace tube. Figure 4.5 shows a schematic of a loading section,
furnace section, and unloading section of a Horizontal tube furnace [23].
56
Figure 4.5: Horizontal tube furnace along with cleaning station [23]
4.7.4
Input variables/ Energy related
The following variables are not directly related with the functionality of the end product,
but do govern energy requirement for an energy intensive process and are described
further.
Wafer diameter
Wafer diameter or size ranges from 3 inches to 12 inches. Most commonly used
sizes in the present fabrication units in United States and in the rest of the electronics
manufacturing world are 6 inches and 8 inches. The number of whole or complete dies is
more on a large size wafer than on a smaller one. This is because; in case of small wafers
there exists a bunch of edge dies. Improving technology demands larger die sizes which
demands for large wafer diameters. A wafer diameter of 6 inches is used for the analysis
purpose. The Oxidizer/Diffuser/Furnace is designed to process a specific diameter range.
The furnace module in consideration can process wafers up to 6 inches in diameter [31].
Capacity and Throughput
The maximum number of wafers a furnace can process in one cycle depends on the
wafer boat size and the number of tubes in the furnace. The horizontal tube furnace has a
capacity to process 200 wafers per cycle. Present day fabrication units have a production
facility to fabricate 10,000 to 35,000 wafers per month. For example, the facility processing
57
10,000 wafers per month will pass the wafers through the process equipment like furnace
several times until the desired number of oxide layers are grown on the wafer in case of
oxidation. Throughput per month implies the number of wafers that will visit the furnace
once in a month for a given process.
Process Cycles/month
Based on the furnace capacity, the oxidation cycles per month can be determined.
For a furnace processing 20,000 wafers per month and a furnace load of 200 wafers per
cycle, there will be 100 production cycles per month. The program estimates the number of
wafers that can be processed per month based on the process time per cycle.
Idle Temperature
Idle temperature of the furnace is the temperature at which the furnace is kept
running idle when not in use. This temperature ranges from 400 ºC to 800 ºC. The wafers
are processed at a stable temperature. The furnace needs sufficient amount of time to
achieve this stability. Also, the quartz tube devitrifies if the furnace is shut down. To avoid
the above problems, the furnace is kept running idle at a lower temperature. The user may
vary the idle temperature form the recommended value of 750 ºC used in the analysis.
Ramp up and ramp down time
The furnace needs to be ramped up to the process temperature from its idle
temperature. As specified by the furnace manufacturer [31], the maximum ramp rate is 22
ºC/min. Higher ramp up rates may result in thermal shock in the furnace tubes and unstable
thermal equilibrium. The user may select the desired ramp up rate from the recommended
range of 5 – 12 ºC/min. After the wafers are oxidized/doped at the process temperature, the
furnace is allowed to slowly cool down. The ramp down rates range from 13 ºC/min for a
temperature range between 1,300 ºC to 1,100 ºC, 8 ºC/min for 1,100 ºC to 900 ºC, and as
low as 5 ºC/min from 900 ºC to idle temperature. Hence the ramp down time is
comparatively very long as compared to the ramp up time. Based on the idle temperature,
process temperature, ramp up and ramp down rates, the total furnace ramp up and ramp
down time can be estimated.
58
Ramp up and ramp down Power and Energy requirement
Based on the time required to ramp up and ramp down the furnace for the
temperature difference (Process temperature – Idle temperature), the power in kW required
can be determined using furnace kVA requirement at any specific temperature. Once the
Power in kW and total ramp time is determined, energy as a function of time in kWh can be
estimated. Hence, energy as a function of time i.e. kWh consumption will be higher when
ramping down the furnace [31].
Layering/Oxidation and Diffusion/Doping process Power and Energy requirement
The process (Oxidation growth/Doping) time determined by the SECALPRO model
in the initial steps is used to estimate the energy required to grow the oxide layer in case of
oxidation process and the energy required for the diffusion time in case of doping process.
Process temperature will govern the power requirement by the furnace for growing or
diffusing.
Total Process time, Furnace Power and Energy requirement
The SECALPRO model can now determine the total process time/cycle and also the
total furnace power and energy requirement per cycle. The energy consumption process
starts right from loading the boat, ramping up the furnace from the idle temperature to the
process temperature, process, and ramping down to idle temperature. This completes one
cycle. The time required to carry out one oxidation/diffusion cycle is termed as the total
process time.
Base level operation and Furnace Controller, Power and Energy requirement
By idle operation it may seem to the reader that the furnace operates in a very low
temperature range. But in semiconductor manufacturing, the furnaces are left running idle
in the temperature range 600 ºC - 800 ºC when not processing any wafers. The energy used
at this idle temperature is independent of the furnace production and will be a constant is
therefore referred to as Base level energy. Although the energy consumed during Base
level operation is part of the furnace energy is estimated separately. While the energy
required for each cycle is estimated separately and is termed as furnace energy.
59
So also is the furnace controller, which will be in operation as long as the furnace is
running, may be at idle or process temperature. As can be seen in Figure 4.7, the area under
the line “Idle Temperature” line depicts the Base level operation energy requirement.
Hence, the furnace will cycle from the idle temperature to the process temperature and back
to the idle temperature when under production.
4.7.5
Furnace/Oxidizer specifications
The next few steps are regarding the furnace/oxidizer specifications. The energy
consumption of a furnace is determined based on the information provided as shown in the
flowchart Figure 4.3.
Oxidizer/Diffuser/Furnace Specifications
Layering/Oxidation and Diffusion/Doping process can be carried out in three
different furnaces, Horizontal tube furnace, Vertical tube furnace, and the Rapid thermal
furnace. Horizontal tube furnace being the basic furnace is widely used. As can be seen in
Table 4.2, the furnace under study is manufactured by Tystar [31]. It can process wafers of
size, maximum 6 inches in diameter. The furnace has the following specifications:
Manufacturer
Type
Max. Electric Power Input
Phase/Frequency
Application
Max.wafer size (dia.)
Max wafers per cycle
Operating Temp. Range
kVA
inches
ºC
TYSTAR Corp.
Horizontal 4 tube furnace
54
3 Phase/50-60Hz
Oxidation, Diffusion, LPCVD
6
200
300 – 1300
Table 4.2: Furnace/Oxidizer specifications [31]
The maximum load or wafers processed per cycle is limited to 200. It can be used for
growing oxides on the wafer as well as for doping wafer at elevated temperatures as high as
1300 ºC.
4.7.6
Energy consuming elements in an Oxidizer/Diffuser/Furnace
The furnace has the following four elements which consume most of the energy as
shown in Figure 4.6
60
Elements of Horizontal four tube furnace
1. Heating coil
2. Exhaust Fans
3. Boat Loader
4. Controller
Figure 4.6: Elements of four tube horizontal furnace
Heating coils are the heating elements which heat the furnace up to the flat zone
temperature. The furnace can have 3 – 5 zones heating. Exhaust fans are used to carry away
the exhaust as well as fumes released during the process. The exhaust is released outside
the plant. Boat loaders are used to load the boat from loading station to the furnace section.
For each tube, a separate boat loader is provided. Controller is used to maintain the
temperature in the furnace as per the process requirement. The controller operates even
when the furnace is running idle. Hence, a large amount of energy is consumed by the
furnace during its idle operation. Heating coils consume electric power to maintain the
desired temperature inside the furnace. The Figure 4.7 shows the effect of temperature on
time required to attain that temperature. At 750 ºC, the wafer boats are loaded into the
furnace tubes. The temperature is held constant for this period of time. Once the boats are
inserted into the furnace tubes, the controller triggers the heating coils to ramp up to the
process temperature. The Figure 4.8 shows the kW consumption by the heating coils
against time. It is clear from the Figure 4.7 and Figure 4.8 that the ramp down time is
longer than the ramp up time [31]. The Table 4.3 shows the kVA consumption of heating
coils per tube for increasing temperature. For example, it can be seen in Table 4.3, that the
furnace consumes 3 kVA per tube at 750 ºC. It also enlists kVA used by the controller, boat
loader, and the furnace exhaust fans.
Furnace Power and Energy consumption
P = 1.732 x V x I x Cosine (Φ)/1000
(4.3)
Energy = P x T
(4.4)
Where;
V = Voltage, Volts
61
A = Amperage, Amperes
Cosine (Φ) = Power factor, 0.9, no units
P = Total Power consumption, kW
T = Time of operation, hrs
Temperature
(ºC)
kVA/Tube
Total kVA
4 Tubes
Controller
kVA
Boat
Loader
kW
400
500
600
700
750
800
850
900
950
1000
1050
1100
1150
1200
1300
1
1.8
2.2
2.8
3
3.2
3.6
4
4.5
4.9
5.5
6.1
6.5
7.2
8.8
4
7.2
8.8
11.2
12
12.8
14.4
16
18
19.6
22
24.4
26
28.8
35.2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
1.5
Furnace Exhaust
Fans
kW
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
Table 4.3: kVA and kW consumption of furnace elements [31]
Temperature (ºC)
TOx/Diff
Oxide Growth/Diffusion
Ramp up
750 Load Boat
Ramp down
Unload Boat
Idle Temperature
Room Temperature
Time (hours)
Figure 4.7: Temperature Levels for Oxidation process [23] * Figure not to scale.
62
KW
Time (hours)
Figure 4.8: kW consumption vs. time per process cycle [23] * Figure not to scale.
4.7.7
Support equipment energy estimation
Along with the furnace, certain equipment are required for wafer handling and
cleaning purposes. These equipment are categorized as support equipment and listed in
Figure 4.9. The rated voltage and amperage for the support equipment are listed in Table
4.4 [32].
Support Equipment
1. Wafer Handler
2. Wafer Cleaner
3. Wafer Scrubber
Figure 4.9: Support equipment used in semiconductor manufacturing
Support Equipments
1. Wafer Cleaner
2. Wafer scrubber
Amperage
(A)
208
208
Voltage
(V)
70
10
Max. Electric Power
Input (kVA)
14.56
2.08
Table 4.4: Support equipment power ratings [32]
Wafer Handler
Wafer boat or cassette movements in the clean room from furnace to wafer cleaner
are carried out with the aid of a wafer handler. The wafer handler is a power arm or a robot,
which transfers the wafer boat to different stations in the clean room. The energy consumed
by wafer handler depends on the number of wafers in the cassette, which it carries. Each
furnace tube can process 50 wafers [31]; hence the wafer handler should carry 50 wafers at
a time. Electric motor is the key energy consuming element in the wafer handler. The
63
SECALPRO model estimates the motor horse power necessary to lift and move the wafer
cassette and also the kW and kWh consumption.
Wafer cleaner
Wafer surface should be clean enough; otherwise the contaminated particles will
remain on the surface and will cause improper functioning of the circuitry. Wafer cleaner is
a cleaning station adjacent to the furnace. It is used to clean the wafers in order to remove
any dust/dirt particles and make them contamination free. It has a hot bath, a rinser, and a
dryer within the single unit. The wafers can also be cleaned using some chemicals. In
semiconductor facilities, the throughput for wafer cleaners range between 150 – 200
wafers/hour. It is kept as a variable in the model and the user may input the relevant
throughput corresponding to the wafer cleaner equipment used. For analysis purpose, the
wafer cleaner can process 150 wafers in an hour. This way, the SECALPRO model can
estimate the relative utilization of wafer cleaner with respect to furnace utilization per
process cycle. In order to estimate the kW and kWh consumption of the wafer cleaner, the
user needs to input the amperage and voltage rating of the cleaner.
Wafer Scrubber
Some organic and inorganic particles on the wafer surface cannot be removed by
the wafer cleaner and are cleaned by using a wafer scrubber. A wafer scrubber is a
polishing unit, which makes the wafer surface shining and free of defects. Wafers are
subjected to surface defects when are cut or diced from the ingot. The wafer scrubber takes
care of any such surface damage. Wafer cleaner and wafer scrubber should clean the wafers
beforehand. While the furnace is in operation, the cleaner and scrubber operate to prepare
the wafers, which are processed in the next cycle. The SECALPRO model calculates the
kW consumed by the support equipment. Depending on the time for which each of the
support equipment operates, the utilization of support equipment with respect to total
process time, and the kWh consumption per equipment is estimated further.
4.7.8
Clean Room design
The oxidation/diffusion process is carried out in the clean room but only the
loading/unloading part of the furnace is exposed to a Class 10 or Class 1. The remaining
64
part of the furnace is in Class 1000 or higher. The wafers are exposed to the environment
only while loading and unloading from the furnace. Support equipment should have a Class
10 or higher as the wafers are exposed to the environment. Particles existing in the clean
room in an excess amount may result in surface contamination of the wafers. The clean
room Class is maintained by circulating air through the clean room several times in an
hour. Recirculation air handlers (RAH) are fans mounted on the clean room top. These fans
are responsible to circulate the air within the room. High Energy Particle Attenuation
(HEPA) filters are used to filter the air coming into the clean room. A FFU is made of one
RAH and one HEPA filter. Air handler is used to maintain the clean room temperatures to
68 – 70 °F (20 - 22 °C) and 45 % Relative humidity [33]. Some facilities might have a
common chiller supplying cold water to the entire facility. Air handler fan pressurizes the
clean room passing through HEPA filters. This helps in maintaining the foreign particles
generated in the clean room to a minimum level. Air handler units are generally roof top
with ducts connecting the air handler fans. Make up fan system is generally installed below
the clean room floor and circulates the clean room air to the Chase and back to the air
handler unit as shown in Figure 4.10. This air mixes with the fresh cold air from the air
handler and is filtered through HEPA filters on its way to clean room. The Figure 4.10 also
shows a modular clean room with Air handler, air handler fan system, HEPA filters,
process tool, chase, and make up fan system. The Figure 4.10 also depicts the air changes
per hour for various clean room classes [34].
Class
10,000
Class
1,000
Class
100
Class
10
Class
1
Air
Changes/hr
75
Air
Changes/hr
195
Air
Changes/hr
360
Air
Changes/hr
420
Air
Changes/hr
450
Figure 4.10: Air changes per hour for a particular clean room Class [34]
65
Process
tool
Chase
Blower
Air handler
Makeup fan system
Figure 4.10 Air changes per hour for a particular clean room Class [34] (Continued)
The Figure 4.11 shows a layout of a clean room used for oxidation/diffusion process. As
seen in Figure 4.11, the clean room cubic feet volume is divided into two classes. As per
the dimension of the furnace and support equipment, the clean room dimensions are
approximated. The Class 1000 is used for the furnace source cabinet and the heat zone.
Wafer loading and unloading takes place in a much cleaner Class i.e. Class 10. The support
equipment is also operated in Class 10. The total cubic feet volume for Class 10 and Class
1000 are 1,650 and 990 respectively [34].
SECALPRO then estimates the number of fan filter units required and its power and
energy consumption to maintain the clean room class conditions based on the following
requirements:
•
Clean room Class desired, no units
•
Clean room volume, cubic feet
Class 1000
Class 10
Figure 4.11: Continued clean room layout
66
11’
9’
3’
10’
6’
7’
15 ’
20 ’
Figure 4.11 Continued Clean room layout (Continued)
No of FFU’s = (air changes per hour/60) x (cubic feet in room/650*)
(4.5)
*CFM output of a loaded Fan Filter Unit (FFU) [34]
The SECALPRO model asks the user to specify the ceiling area of clean room in
square feet apart from the cubic feet volume. This helps it determine the total number of air
handler units, air handler fans, and make up fans. The clean room conditions are
maintained 24 hours a day, 7 days a week, throughout the year irrespective of the
production. After determining the number of cooling and fan units and also the monthly
usage, SECALPRO then estimates the total energy (kWh) usage by the clean room.
Estimation of cooling load
Equipment, operators and fan filter units are the elements, which release heat when
in operation. For equipment such as the furnace used, it is estimated that the insulation
efficiency is 95% from 3 - E Plus software version 3.2 (North American Insulation
Manufacturers Association). Hence, about 5% of the energy consumed by the equipment is
released (radiated) as a cooling load.
From ASHRAE Fundamentals book, every human being releases around 800
Btu/hr. As per the information released from Terra Universal, manufacturers of clean
67
rooms and clean room equipment, approximately 1000 Btu/hr of heat is released from a
single FFU [34]. All of these elements constitute to the cooling load in the clean room.
4.7.9
Total kW and kWh consumption
The last and the final steps are to determine the kW and kWh consumption per
month and also the energy consumed per wafer. Energy consumed per wafer depends on
the wafer throughput per month.
4.8
Conclusion
This chapter discusses the necessity to model the energy requirement of energy
intensive processes in semiconductor manufacturing and a systematic approach to model it.
It also discusses the individual components of the SECALPRO model in detail. The
developed SECALPRO model will enable the user to select the desired energy intensive
process and estimate its energy requirement beforehand. Input to the SECALPRO model
will be the process and production parameters. Also, the support energy requirement can be
estimated. The user can thus vary the input parameters to the SECALPRO model and study
its effect on energy utilization. Hence, the user can select the process as well as production
parameters to minimize the process energy requirement. The SECALPRO model is capable
of estimating the cooling load and also the clean room energy requirement subject to its
Class and cubic feet volume. Therefore, the total energy requirement in kWh per wafer as
well as the additional energy in terms of clean room can be estimated for the selected
process.
68
Chapter 5
5
5.1
Results and Sensitivity Analysis
Oxidation
The following parameters affect energy usage in Oxidation growth on silicon
wafers. A sensitivity analysis is done to study their effect. Sensitivity analysis involves
varying the input parameters to an extent of practical feasibility and is also restricted to
sustain the device functionality.
5.1.1
Temperature effect
A change in process temperature alters the total time for the oxidation/layering
process. The total time comprises of the boat loading and unloading time, ramp up time,
oxide growth time, and the ramp down time. As stated earlier that oxide growth obeys an
Arhenius relationship [26] which relates temperature with the growth time. The oxide
growth time reduces exponentially with the increase in temperature. Hence, in order to
reduce the oxide growth time, temperature should be increased to an extent of practical
feasibility. This extent or upper limit for the temperature depends on the thickness of oxide
to be grown. At the same time when it is desired to increase the temperature, the time
required to ramp up and ramp down (excluding process time) the furnace increases. Since,
the process time is dominant with respect to the ramp up and ramp down time, the total
time does reduce with the increase in temperature but is limited by the thickness of oxide
grown.
For Oxide thickness of 0.1 micron
This effect is shown in the Figure 5.1 for growing 0.1 micron of oxide on a bare
silicon wafer. For small oxide thickness of 0.1 micron, the total process time reduces for a
temperature range of 900 – 950 °C and further increases with temperature rise. Power
consumed in kW by the furnace is independent of the total time but varies with the
temperature. Energy in kWh is dependent on the power consumed and the total time. The
Table 5.1 shows the results from the sensitivity of process temperature and Energy (kWh)
consumption for 0.1 micron oxide growth. It is clear that energy as a function of time
depicts similar type of relationship as that of total process time with temperature. Hence
69
from the analysis for growing oxide of 0.1 micron thickness, the maximum temperature at
which the wafers could be processed so as to achieve minimum energy consumption is 950
°C.
Time vs. Temperature - 0.1 micron oxide
1.8
1.6
Time - hours
1.4
1.2
1
0.8
0.6
0.4
0.2
0
900
950
1000
1050
Total time
1.591525
1.397054
1.422268
1.53532
Process time
0.708191
0.326221
0.163935
0.089486
Excluding Process time
0.883334
1.070833
1.258333
1.445834
Temperature - Degree Celcius
Figure 5.1: Time vs. temperature – 0.1 micron, 900 – 950 °C
From the Table 5.1, it is clear that a facility growing 0.1 micron oxide at 900 °C can
process approximately 84,447 wafers per month and the energy requirement per wafer
would be 0.4 kWh. Increasing the process temperature from 900 °C to 950 °C will reduce
the total process time from 1.59 hours to 1.39 hours. Due to the reduction in the total
process time, it becomes possible to process 96,202 wafers per month and the energy
requirement per wafer reduces to 0.376 kWh. The energy savings per month, energy
savings per wafer, percentage energy savings per wafer, and the percentage excess wafers
processed per month as a result of increasing the process temperature from 900 °C to 950
°C for growing 0.1 micron oxide will be;
Energy (kWh) savings/month = 96,202 wafers per month x (0.4 - 0.376) kWh/wafer
= 2,309 kWh/month
= 27,708 kWh/yr
= 95 MMBtu/yr
70
Energy (kWh) savings per wafer = 0.4 kWh - 0.376 kWh
= 0.024 kWh/wafer
Percentage energy savings per wafer = (0.4 kWh - 0.376 kWh)/ 0.4 kWh x 100
=6%
Percentage excess wafers processed per month = (96,202 - 84,447)/84,447 x 100
= 13.92 %
Throughput (Wafers/month)
Oxidation cycles per month
Wafer Diameter (inches)
Oxide Thickness (microns)
Type (Wet - w/Dry - d)
Orientation ( <100> - o, <111> - i)
Oxidation Temperature °C
Furnace Idle Temp °C
Ramp up rate (°C/minute)
Total Ramp Up Time (hrs)
Oxidation Process Time (hrs)
Total Ramp Down Time (hrs)
Total Process Time (hrs)
Excluding Process time (hrs)
Furnace kW consumption
Total Support Equipment kW consumption
Total Clean room Cooling load due to equipment
and operators in kW
Controller and Base Level kW consumption
Total process kW consumption
Total kW consumption per wafer
Air handling unit, Air handler fan and Makeup
fan kW consumption
Total Fan filter unit kW consumption
Heat released from Fan filter units in kW
Additional kW consumption by the Clean room
Furnace kWh consumption per month
Total Support Equipment kWh consumption per
month
Controller and Base Level kWh consumption per
month
Total Clean room Cooling load due to equipment
and operators in kWh per month
Total process kWh consumption per month
Total kWh consumption per wafer
Air handling unit, Air handler fan and Makeup
fan kWh consumption per month
Total Fan filter unit kWh consumption per month
Heat released from Fan filter units per month
84,447.33
422.24
6
0.1
w
o
900
750
10
0.32
0.71
0.57
1.592
0.88
49.79
30.88
96,202.41
481.01
6
0.1
w
o
950
750
10
0.40
0.33
0.67
1.397
1.07
51.80
30.88
94,496.93
472.48
6
0.1
w
o
1000
750
10
0.48
0.16
0.78
1.422
1.26
53.52
30.88
87,538.78
437.69
6
0.1
w
o
1050
750
10
0.57
0.09
0.88
1.535
1.45
55.88
30.88
4.72
4.82
4.91
5.03
13.79
99.18
0.00118
13.79
101.30
0.00105
13.79
103.10
0.00109
13.79
105.58
0.00121
12.46
12.46
12.46
12.46
8.40
6.66
27.52
6,900.78
8.40
6.66
27.52
6,897.37
8.40
6.66
27.52
6,829.33
8.40
6.66
27.52
6,881.54
16,228.63
18,282.94
17,984.89
16,768.89
9,268.91
9,268.91
9,268.91
9,268.91
1,619.92
1,722.46
1,704.16
1,645.97
34,018.24
0.4028
36,171.68
0.3760
35,787.28
0.3787
34,565.31
0.3949
8,375.96
8,375.96
8,375.96
8,375.96
5,645.68
4,473.33
5,645.68
4,473.33
5,645.68
4,473.33
5,645.68
4,473.33
Table 5.1: Results – 0.1 micron, 900 – 950 °C
71
As seen in Table 5.1, the Total process kWh consumption per month is comprised of the
energy consumed by the Furnace, Support equipment, Base level and controller, and the
clean room cooling load due to equipment and the operators. Also seen in Table 5.1,
additional energy is required to maintain the clean room class conditions using Air handler
units, Makeup fans and Fan filter units. This additional energy used is independent of the
operation in the clean room and is therefore a constant. Hence, it is not accounted in the
estimation of energy (kWh) requirement per wafer. Figure 5.2 depicts the percentage
energy consumption per month for growing 0.1 micron oxide at various temperatures. The
total energy consumed by the furnace is the summation of the Furnace energy and the Base
level and controller energy. Increasing the process temperature from 900 °C to 950 °C
reduces the total Furnace energy but increases the support equipment energy. It is because,
increasing the process temperature reduces the process time and hence the furnace can
process more number of wafers with reduced energy consumption. At the same time, the
support equipment is required to process the increased number of wafers. Hence, the
support equipment energy increases with the increase in process temperature.
% Energy per month vs. Temperature - 0.1 micron oxide
% Energy consumption per month
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Clean room cooloing load due to
equipment & operators
900.00
950.00
1,000.00
1,050.00
1,619.92
1,722.46
1,704.16
1,645.97
Controller and Base level
9,268.91
9,268.91
9,268.91
9,268.91
Support equipment
16,228.63
18,282.94
17,984.89
16,768.89
Furnace
6,900.78
6,897.37
6,829.33
6,881.54
Temperature - Degree Celcius
Figure 5.2: Percentage energy distribution – 0.1 micron, 900 – 950 °C
72
Referring Figure 5.2, for a process temperature of 950 °C, the total furnace energy
requirement will be 16,166.28 kWh (9,268.91 kWh + 6,897.37 kWh). Energy in kWh, a
function of the total process time shows similar type of pattern. It is evident in Figure 5.1
and Figure 5.3. Figure 5.3 depicts the energy (kWh) per wafer vs. process temperature
relationship. The clean room cooling load due to the equipment and operators varies with
the process temperature.
Energy (kWh) per wafer vs. Temperature - 0.1 micron oxide
0.4050
0.4000
Energy (kWh) per wafer
0.3950
0.3900
0.3850
0.3800
0.3750
0.3700
0.3650
0.3600
Energy (kWh) per wafer
900.00
950.00
1,000.00
1,050.00
0.4028
0.3760
0.3787
0.3949
Temperature - Degree Celcius
Figure 5.3: Energy (kWh) consumption per wafer vs. temperature – 0.1 micron oxide growth
For Oxide thickness of 1 micron
The temperature effect is more lucrative and fruitful for thicker oxides. From the
Figure 5.4, it is seen that, the process time is more dominant than the ramp up and ramp
down time (Excluding process time). Hence, it is observed that the total time decreases for
the given temperature range of 950 °C – 1200 °C. In production facilities, wafers are
oxidized in a temperature vicinity of 1000 °C. Also, processing wafers close to 1300 °C
which is close to the melting point of silicon is not advisable and may create permanent
distortions in wafer structure. Hence processing wafers at 1150 °C instead of 1000 °C, will
abide by the practical limitations posed by the complex process. From energy point of
view, a temperature above 1150 °C does not really yield much high energy savings but lie
73
in the same range. Hence, it is recommended to process wafers at 1150 °C when an oxide
thickness of 1 micron is to be achieved.
Time vs. Temperature - 1 micron oxide
9
8
Time (hours)
7
6
5
4
3
2
1
0
950
1000
1050
1100
1150
1200
8.126608 5.735033 4.509956 3.852369 3.456673 3.234751
Total Time
Excluding Process Time 1.070834 1.258333 1.445833 1.633333 1.780769 1.928205
7.055774
4.4767
3.064123 2.219036 1.675904 1.306546
Process Time
Temperature - Degree Celcius
Figure 5.4: Time vs. temperature –1 micron, 1000 – 1150 °C
Referring Table 5.2, The energy savings per month, energy savings per wafer, percentage
energy savings per wafer, and the percentage excess wafers processed per month as a result
of increasing the process temperature from 1000 °C to 1150 °C for growing 1 micron oxide
will be;
Energy (kWh) savings/month = 38,881 wafers per month x (1.059 - 0.735) kWh/wafer
= 12,597 kWh/month
= 151,169 kWh/yr
= 516 MMBtu/yr
Energy (kWh) savings per wafer = 1.059 kWh – 0.735 kWh
= 0.324 kWh/wafer
Percentage energy savings per wafer = (1.059 kWh – 0.735 kWh)/ 1.059 kWh x 100
= 30.59 %
Percentage excess wafers processed per month = (38,881 - 23,435)/ 23,435
= 65.9 %
74
Throughput (Wafers/month)
Oxidation cycles per month
Wafer Diameter (inches)
Oxide Thickness (microns)
Type (Wet - w/Dry – d)
Oxidation Temperature °C
Furnace Idle Temp °C
Ramp up rate (°C/minute)
Total Ramp Up Time (hrs)
Oxidation Process Time (hrs)
Total Ramp Down Time (hrs)
Total Process Time (hrs)
Excluding Process time (hrs)
Furnace kW consumption
Support Equipment kW consumption
Total Clean room Cooling load due to equip. and op. in kW
Total Controller and Base Level kW consumption
Total process kW consumption
Total kW consumption per wafer
Air handling unit, Air handler fan and Makeup fan kW consumption
Total Fan filter unit kW consumption
Heat released from Fan filter units in kW
Additional kW consumption by the Clean room
Furnace kWh consumption per month
Total Support Equipment kWh consumption per month
Controller and Base Level kWh consumption per month
Total Clean room Cooling load due to equip. and op in kWh per month
Total process kWh consumption per month
Total kWh consumption per wafer
Air handling unit, Air handler fan and Makeup fan kWh/per month
Total Fan filter unit kWh consumption per month
Heat released from Fan filter units per month
16,538.27
82.691331
6
1
w
950
750
10
0.40
7.06
0.67
8.13
1.07
51.80
30.88
4.82
13.79
101.30
0.01
12.46
8.40
6.66
27.52
8,383.09
4,360.92
9,268.91
1,100.65
23,113.56
1.398
8,375.96
5,645.68
4,473.33
23,434.91
117.17457
6
1
w
1000
750
10
0.48
4.48
0.78
5.74
1.26
53.52
30.88
4.91
13.79
103.10
0.00
12.46
8.40
6.66
27.52
8,802.61
5,566.17
9,268.91
1,181.88
24,819.57
1.059
8,375.96
5,645.68
4,473.33
29,800.73
149.00366
6
1
w
1050
750
10
0.57
3.06
0.88
4.51
1.45
55.88
30.88
5.03
13.79
105.58
0.00
12.46
8.40
6.66
27.52
9,331.55
6,678.65
9,268.91
1,263.96
26,543.07
0.891
8,375.96
5,645.68
4,473.33
Table 5.2: Results –1 micron, 1000 – 1150 °C
75
34,887.62
174.43812
6
1
w
1100
750
10
0.65
2.22
0.98
3.85
1.63
58.29
30.88
5.15
13.79
108.11
0.00
12.46
8.40
6.66
27.52
9,659.61
7,567.63
9,268.91
1,324.81
27,820.96
0.797
8,375.96
5,645.68
4,473.33
38,881.31
194.40656
6
1
w
1150
750
10
0.73
1.68
1.05
3.46
1.78
60.10
30.88
5.24
13.79
110.01
0.00
12.46
8.40
6.66
27.52
9,688.86
8,265.56
9,268.91
1,361.17
28,584.50
0.735
8,375.96
5,645.68
4,473.33
41,548.79
207.74394
6
1
w
1200
750
10
0.82
1.31
1.11
3.23
1.93
62.82
30.88
5.37
13.79
112.87
0.00
12.46
8.40
6.66
27.52
9,923.88
8,731.73
9,268.91
1,396.23
29,320.75
0.706
8,375.96
5,645.68
4,473.33
% Energy consumption per month
% Energy per month vs. Temperature - 1 micron oxide
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
950
1000
1050
1100
1150
1200
Clean room cooling load due to
Equipment and Operators
1,100.65
1,181.88
1,263.96
1,324.81
1,361.17
1,396.23
Controller and Base Level
9,268.91
9,268.91
9,268.91
9,268.91
9,268.91
9,268.91
Support Equipment
4,360.92
5,566.17
6,678.65
7,567.63
8,265.56
8,731.73
Furnace
8,383.09
8,802.61
9,331.55
9,659.61
9,688.86
9,923.88
Temperature - Degree Celcius
Figure 5.5: Percentage energy distribution – 1 micron, 1000 – 1150 °C
Referring Figure 5.5, at a process temperature of 1150 °C, the total furnace energy
requirement will be 18,957.77 kWh (9,268.91 kWh + 9,688.86 kWh). It s evident from
Figure 5.6 that processing wafers at 1150 °C when growing 1 micron thick oxide will require
0.735 kWh per wafer as against 1.06 kWh at 1000 °C.
Energy (kWh) per wafer vs. Temperature - 1 micron oxide
1.60
Energy (kWh) per wafer
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Energy (kWh) per wafer
950
1000
1050
1100
1150
1200
1.40
1.06
0.89
0.80
0.74
0.71
Temperature - Degree Celcius
Figure 5.6: Energy (kWh) consumption per wafer vs. temperature – 1 micron oxide growth
76
For Oxide thickness of 0.25 micron
Time (hours) vs. Temperature - 0.25 oxide
3.5
3
Time (jours)
2.5
2
1.5
1
0.5
0
900
950
1000
1050
1100
1150
Process Time
1.987111
0.973618
0.528061
0.314101
0.202419
0.139432
Excluding Process Time
0.883333
1.070833
1.258333
1.445834
1.633333
1.780769
Total Time
2.870444
2.044451 1.786394 1.759935 1.835752
Temperature - Degree Celcius
1.920201
Figure 5.7: Time vs. temperature – 0.25 micron, and 950 – 1050 °C
kWh/month saved
3,205.64
kWh/yr saved
38,467.62
MMBtu/yr saved
131.29
kWh savings per wafer
0.04
% energy savings per wafer
8.85 %
% excess wafers per month
16.17 %
Table 5.3: Energy savings and productivity improvement for 0.25 micron oxide growth, 950 – 1050 °C
Figure 5.7 depicts the time –temperature relationship observed in growing 0.25 micron thick
oxide. The process can be carried out in a minimum total time of 1.759 hours at 1050 °C. As
can be seen in Figure 5.7, increasing temperature beyond 1050 °C does not help in reducing
the total time required by the process. Hence, it is recommended to operate the furnace at
1050 °C to achieve minimum process time, and maximum energy savings. Also, from Table
5.3, processing wafers at 1050 °C instead of 950 °C will result in energy saving of 0.04 kWh
per wafer. The percentage energy savings per wafer will be approximately 9 % and the
percentage excess wafers processed per month will be approximately 16 %.
77
For Oxide thickness of 0.5 micron
From Figure 5.8, it is clear that, growing 0.5 micron thick oxide at 1150 °C will
minimize the process time. Table 5.4 shows that, processing wafers at 1150 °C instead of
1000 °C will result in energy saving of 0.07 kWh per wafer. The percentage energy savings
per wafer will be approximately 11 % and the percentage excess wafers processed will be
approximately 21 %.
Time vs. Temperature - 0.5micron oxide
4
3.5
3
Time (hours)
2.5
2
1.5
1
0.5
0
950
1000
1050
1100
1150
1200
Total Time
3.544953
2.708531
2.375322
2.273064 2.245995
2.281077
Process Time
2.474119
1.450198
0.929489
0.639731 0.465226
0.352872
1.258333 1.445833 1.633333 1.780769
Temperature - Degree Celcius
1.928205
Excluding Process Time 1.070834
Figure 5.8: Time vs. temperature – 0.5 micron, 1000 – 1150 °C
kWh/month saved
3,965.83
kWh/yr saved
47,589.91
MMBtu/yr saved
162.42
kWh savings per wafer
0.07
% energy savings per wafer
11.39%
% excess wafers per month
20.59%
Table 5.4: Energy savings and productivity improvement for 0.5 micron oxide growth
5.1.2
Throughput effect
In almost all industrial facilities, the furnace and the support equipment operate for at
least 672 hours/month (24 hours/day x 7 days/week x 4 weeks’/month). Depending on the
oxide thickness to be grown, the total process time is estimated. As mentioned earlier, the
throughput per month implies the number of wafers that will visit the furnace once in a
78
month for a given process. For devices like MOSFET, the wafer may undergo oxidation
process for approximately 4 times and hence needs to make 4 visits to the furnace. As
mentioned earlier, that the oxide growth time and thus the total process time is a function of
the process temperature, obeying the Arhenius type relationship [26]. There exists a
temperature at which the total process time is minimum for a given oxide thickness. Hence,
the furnace can cycle more number of times in a given period of time at that temperature,
thus increasing the number of wafers processed. For example, referring Table 5.1 again for
growing 0.1 micron thick oxide,
At 900 °C, Total Process time per cycle = 1.592 hours
In 672 hours/month, the total number of process cycles = 672 hours/month/1.592 hour/cycle
= 422 cycles/month
Number of wafers processed per month = 422 cycles/month x 200 wafers/cycle
= 84,447 wafers/month
Similarly,
At 950 °C, Total Process time per cycle = 1.397 hours
In 672 hours/month, total number of process cycles = 672 hours/month/1.397 hours
= 481 cycles/month
Number of wafers processed per month = 481 cycles/month x 200 wafers/cycle
= 96,202 wafers/month
Hence, as can be seen in Figure 5.9, the maximum number of wafers on which 0.1 micron
thick oxide can be grown is approximately 96,202 at 950 °C. Increasing the process
temperature beyond 950 °C will increase the process time per cycle and hence decrease the
production. Therefore with an increase in throughput, the energy usage (kWh) per wafer will
reduce for the temperature range 900 °C to 950 °C.
79
Throughput vs. Temperature - 0.1 micron oxide
Throughput - wafers processed per month
98,000
96,000
94,000
92,000
90,000
88,000
86,000
84,000
82,000
80,000
78,000
Throughput
900.00
950.00
1,000.00
1,050.00
84,447.33
96,202.41
94,496.93
87,538.78
Temperature - Degree Celcius
Figure 5.9: Throughput vs. temperature – 0.1 micron oxide growth
Throughput vs. Temperature - 1 micron oxide
Throughput - wafers processed per month
45,000.00
40,000.00
35,000.00
30,000.00
25,000.00
20,000.00
15,000.00
10,000.00
5,000.00
0.00
Throughput
950
1000
16,538.27
23,434.91
1050
1100
1150
1200
29,800.73
34,887.62
38,881.31
41,548.79
Temperature - Degree Celcius
Figure 5.10 Throughput vs. temperature –1 micron oxide growth
As seen in Figure 5.10, the maximum number of wafers on which 1 micron thick oxide can
be grown is approximately 38,881 at 1150 °C. Increasing the process temperature beyond
1150 °C is though not recommended, but will increase the throughput per month.
80
5.1.3
Ramp rate Effect
Ramp up and ramp down time are furnace ramp rate, idle temperature, and process
temperature dependent. The higher the ramp up rate, the lesser the time required to achieve
the process temperature and higher the power consumption. The maximum ramp up rate for
the furnace under study is 22 ºC/min. High ramp up rate may result in thermal shock in the
furnace tubes. High temperature gradient may cause the wafers to distort, which is not a
desirable condition in semiconductor manufacturing. Hence, the ramp rate is kept as a
variable which will enable the user to analyze its effect on energy and process time. Table 5.5
lists the ramp up and ramp down rates specified by the furnace manufacture and used in the
analysis [31]. A ramp up rate of 10 ºC/min and ramp down rates of 13, 8, and 5 ºC/min are
used in the SECALPRO model.
Temperature range
(ºC)
Idle temp – 900
900 – 1,100
1,100 – 1,300
Ramp Up Rate
(ºC/min) Max -22
10
10
10
Temperature range
(ºC)
1,300 – 1,100
1,100 – 900
900 – Idle temp.
Ramp down Rate
(ºC/min)
13
8
5
Table 5.5: Ramp up and ramp down rates used in the SECALPRO model [31]
Table 5.6 shows the effect of varying the ramp up rate on the energy usage (kWh) per wafer.
Increasing the ramp up rate from 8 ºC/min to 10 ºC/min as well as to 12 ºC/min increases the
furnace energy consumption. The reason behind this behavior is the increase in power (kW)
consumption to maintain the higher ramp up rate. The furnace has heater coils which
maintain the temperature inside the tube across its length. The controller for individual tube
increases the current flow through these coils to achieve the new ramp up rate. Also, the
ramp up time reduces with the increase in ramp up rate thus reducing the total process time
per cycle. The overall effect is the reduction in process time at an expense of extra energy. It
is important to note that, the higher the ramp up rate, the more will be the energy losses into
the clean room, thus increasing the total energy usage per wafer. From Table 5.6, for a ramp
up rate of 8 ºC/min, the total process time is 3.623 hours as against 3.457 hours at 10 ºC/min
when growing 1 micron thick oxide. Hence, the productivity increase per month due to
increase in ramp up rate from 8 ºC/min to 10 ºC/min will be,
= (38,881.31 - 37,092.85)/ 37,092.85 x 100
=5%
81
But the increase in energy usage per wafer = (0.735 – 0.672)/0.672 x 100
= 10 %
Throughput (Wafers/month)
Oxidation cycles per month
Wafer Diameter (inches)
Oxide Thickness (microns)
Type (Wet - w/Dry – d)
Oxidation Temperature °C
Furnace Idle Temp °C
Ramp up rate (°C/minute)
Total Ramp Up Time (hrs)
Oxidation Process Time (hrs)
Total Ramp Down Time (hrs)
Total Process Time (hrs)
Furnace kWh consumption per month
Total Support Equipment kWh consumption per month
Controller and Base Level kWh consumption per month
Total Clean room Cooling load due to equipment and op. in
kWh/month
Total process kWh consumption per month
Total kWh consumption per wafer
37,092.85
185.46
6
1
w
1150
750
8
0.900
1.676
1.047
3.623
7,752.19
7,953.01
8,043.63
38,881.31
194.40
6
1
w
1150
750
10
0.733
1.676
1.047
3.457
9,688.86
8,265.56
9,268.91
40,172.62
200.86
6
1
w
1150
750
12
0.622
1.676
1.047
3.346
11,627.45
8,491.23
10,494.18
1,187.44
1,361.17
1,530.64
24,936.28
0.672
28,584.50
0.735
32,143.51
0.800
Table 5.6: Effect of ramp up rate on energy savings and productivity improvement – 1 micron oxide
growth
Ramp up time vs. Ramp up rate
0.85
Ramp up time (hours)
0.80
0.75
0.70
0.65
0.60
0.55
0.50
Ramp up time
8
0.83
10
12
0.67
0.56
Ramp up rate (C/min)
Figure 5.11: Time vs. temperature – 1 micron, 8 ºC/min, 10 ºC/min, 12 ºC/min RUR
The total ramp up time as seen in Table 5.6 includes the time required to load the boat and
the time required to ramp from idle temperature 750 ºC to the process temperature 1150 ºC.
82
For example, the boat loading speed is 15 inch per minute and it takes 4 minutes to load the
boat in a 60 inch long tube [31]. Hence the total ramp up time (0.9 hours) for 8 ºC/min ramp
up rate will be the summation of boat loading time (4 minute/60 minutes/hour = 0.067 hours)
and ramp up time (1150 ºC - 750 ºC = 450 ºC, 450 ºC/8 ºC/min = 0.833 hours). The impact
of high ramp up rate on ramp up time and energy consumption is more obvious for higher
ramp up rates and can be seen in Figure 5.11. Hence, ramp up rate should only be increased
to achieve productivity savings.
5.2
Doping
The following parameters affect energy usage in doping the silicon wafers. A
sensitivity analysis is done to study their effect.
5.2.1
Dopant Effect
Two types of dopant are used for the analysis purpose. Boron is a commonly used P
type dopant, whereas, Phosphorus as an N type. As per the Arhenius type of relationship, the
diffusivity of one element into the other depends on the diffusion coefficient, diffusion
temperature, and the activation energy to carry out the diffusion process. Since pre –
deposition time is very less as compared to the drive-in time; therefore the parameters are
changed only in accordance to the drive-in process.
Using P type – Boron, and N type – Phosphorus dopant
For a predeposition time of 10 minutes (0.1666 hours) and pre – deposition
temperature of 900 °C, the dopant atoms can reach to a depth of 0.039 µm. Once the atoms
enter the wafer, they are further driven by heating the wafers at a higher temperature in drivein step. For drive-in parameters of 1100 °C and 5 hours, the dopant atoms can reach to a
depth of 2.08 microns for P type dopant Boron. Hence, the junction depth achieved is 2.08
microns. If, N type dopant – Phosphorus is used, the depth at which the dopant atoms will
settle after the predeposition step is nearly the same as that in the previous case for Boron
because of short predeposition time. But, the junction depth achieved is 2.45 microns after
the drive-in step. The reason behind this is the different value for the diffusion coefficient
and the activation energy for the dopant types.
83
Dopant type
Dopant
Diffusion coefficient Do
(cm2/sec)
Activation energy Ea (eV)
P
N
Boron
Phosphorus
1
4.7
3.5
3.68
Table 5.7: Diffusion coefficient and activation energy values for P – type, and N – type doping [35]
From Table 5.7, it is evident that the diffusivity of N type dopant Phosphorus into the silicon
wafer will be more than that could be achieved from P type Boron. This high rate of diffusion
can be realized at a very low increase of activation energy. Although, the type of dopant to be
used depends on the end use and functionality desired from the component, the effect of
dopant type on energy usage is discussed further. Therefore, in 5 hours, a junction depth of
2.08 microns can be achieved using P type dopants and a junction depth of 2.45 microns can
be achieved using N type dopants at 1100 °C as listed in Table 5.8.
Dopant
type
Dopant
Junction depth
(microns)
P
N
Boron
Phosphorus
2.087516
2.450627
Drive-in
process
temperature
(°C)
1100
1100
Drive-in
process
time (hrs)
kWh consumption
per wafer
5
1.28
Table 5.8: Effect of dopant type on junction depth
The energy consumption per wafer for the given set of parameters will be the same
for P type as well as N type doping. But for the same process time, the junction depth
achieved will be higher for N type doping. In other words, for the desired junction depth, the
time required using N type dopant will be less than using P type dopant, and hence the total
process time will be less reducing the energy consumption per wafer.
5.2.2
Process time effect
Table 5.9 describes the effect of drive-in process time on Junction depth achieved.
Increasing the drive-in time from 5 hours [28] to 10 hours results in an increase of only 0.73
microns (2.82 – 2.08) for P type doping. On the other hand an increase of 0.93 microns can
be achieved in case of N type doping. Hence, increasing the drive-in process time to achieve
higher junction depth is uneconomical.
84
Drive-in
Time
Temperature °C
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
(hrs)
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
Junction depth Xj (µm)
P Type N Type Boron
Phosphorus
2.0836171
2.4507153
2.1726758
2.5823432
2.2571657
2.6866947
2.3376677
2.7863335
2.4146506
2.8818088
2.4884994
2.9735716
2.5595341
3.0619979
2.6280249
3.1474046
2.6942016
3.2300622
2.7582619
3.3102033
2.8203775
3.3880297
Table 5.9: Effect of drive-in time on junction depth for P type and N type doping
5.2.3
Process temperature effect
As discussed before, doubling the drive-in time does not result in proportionate
increase in the junction depth. But it is found that, the drive-in process time can be
significantly reduced by reasonably increasing the drive-in temperature. Referring Table
5.10, for a drive-in time of 5 hours, the junction depth achieved is 2.08 microns and the
energy consumption per wafer is 1.28 kWh for P type doping. Similarly for 10 hour drive-in
time, the junction depth achieved is 2.82 microns and the energy consumption per wafer is
2.16 kWh for P type doping.
Dopant
type
Dopant
Junction depth
(microns)
P
N
P
N
Boron
Phosphorus
Boron
Phosphorus
2.087516
2.450627
2.826205
3.355891
Drive-in process
temperature
(°C)
1100
1100
1100
1100
Drive-in process
time (hrs)
kWh
consumption
per wafer
5
1.28
10
2.16
Table 5.10: Temperature and drive - in time effect on junction depth
From Table 5.11, it is seen that, if the drive in temperature is increased from 1100 °C to 1150
°C, the drive in process time required achieving the same junction depth of 2.087516 microns
for P type dopant – Boron is 2.60 hours instead of 5 hours at 1100 °C. Also, the kWh usage
per wafer reduces from 1.28 kWh at 1100 °C to 0.902 kWh at 1150 °C.
85
Dopant
type
Dopant
Junction depth
(microns)
P
Boron
N
Phosphorus
2.087516
2.087516
2.450627
2.450627
Drive-in process
temperature
(°C)
1100
1150
1100
1150
Drive-in process
time (hrs)
5
2.60
5
1.91
kWh
consumption
per wafer
1.28
0.902
1.28
0.777
Table 5.11Temperature effect on drive - in time and energy usage
Hence, the resulting energy savings and percentage excess wafers processed due to increase
in drive in process temperature from 1100 °C to 1150 °C for P type dopant - Boron would be;
Energy savings per wafer = (1.28 kWh - 0.902 kWh)
= 0.378 kWh/wafer
Percentage energy savings per wafer = (1.28 kWh - 0.902 kWh)/ 1.28 kWh x 100
= 29.5 %wafer
Referring Table 5.12, the throughput per month for a drive in time of 5 hours and 2.6 hours
per cycle is 20,261.31 and 30,679.54 wafers respectively.
Percentage excess wafers per month = (30,679- 20,261)/20,261 x 100
= 51 %
P
Boron
0.1666
900
5
1100
1E+16
2.087516
20,261.31
101.306533
6
750
10
0.65
5
Dopant
Pre deposition time (hrs)
Pre deposition temperature (°C)
Drive-in time (hrs)
Drive-in temperature (°C)
Substrate concentration (atoms/cm^3)
Junction Depth (microns)
Throughput
Doping cycles per month
Wafer Diameter (inches)
Furnace Idle Temp (°C)
Ramp Up Rate (°C/min)
Total Ramp Up Time (hrs)
Doping Process Time (hrs)
86
P
Boron
0.1666
900
10
1100
1E+16
2.826138
11,553.01
57.765043
6
750
10
0.65
10
P
Boron
0.1666
900
2.6
1150
1E+16
2.470744
30,679.54
153.397717
6
750
10
0.733333
2.6
Total Ramp Down Time (hrs)
Total Process Time (hrs)
Total Furnace kW consumption
Total Support Equipment kW consumption
Total Clean room Cooling load due to equip. and op. in kW
Total Controller and Base Level kW consumption
Total process kW consumption
Total kW consumption per wafer
Air handling unit, Air handler fan and Makeup fan kW
consumption
Total Fan filter unit kW consumption
Heat released from Fan filter units in kW
Additional kW consumption by the Clean room
Hence, Total Furnace kWh consumption per month
Total Support Equipment kWh consumption per month
Total Controller and Base Level kWh consumption per month
Total Clean room Cooling load due to equip. and op. in kWh per
month
Total process kWh consumption per month
Total kWh consumption per wafer
Air handling unit, Air handler fan and Makeup fan kWh/ month
Total Fan filter unit kWh consumption per month
Heat released from Fan filter units per month
Additional kWh consumption by the Clean room
0.983
6.633
58.289
30.882
5.148
13.793
108.113
0.005
0.983
11.633
58.289
30.882
5.148
13.793
108.113
0.009
1.047
4.381
60.100
30.882
5.239
13.793
110.014
0.004
12.464
8.401
6.657
27.522
10,531.31
5,011.55
9,268.91
12.464
8.401
6.657
27.522
11,050.30
3,489.70
9,268.91
12.464
8.401
6.657
27.522
10,282.00
6,832.23
9,268.91
1,240.59
26,052.36
1.286
8,375.96
5,645.68
4,473.33
18,494.96
1,190.45
24,999.36
2.164
8,375.96
5,645.68
4,473.33
18,494.96
1,319.16
27,702.29
0.903
8,375.96
5,645.68
4,473.33
18,494.96
Table 5.12: Results –P type dopant, drive-in time 2.6, 5, 10 hours, and temperature 1100 - 1150 °C
Similarly for N type dopant - Phosphorus, referring Table 5.13, the resulting energy savings
and percentage excess wafers processed due to increase in drive in process temperature from
1100 °C to 1150 °C for N type dopant – Phosphorus would be;
Energy savings per wafer = (1.28 kWh – 0.77 kWh)
= 0.51 kWh/wafer
N
Phosphorus
0.1666
900
5
1100
1.00E+16
2.450587
20,261.31
101.306533
6
750
Dopant
Pre deposition time (hrs)
Pre deposition temperature (°C)
Drive-in time (hrs)
Drive-in temperature (°C)
Substrate concentration (atoms/cm^3)
Junction Depth (microns)
Throughput
Doping cycles per month
Wafer Diameter (inches)
Furnace Idle Temp (°C)
87
N
Phosphorus
0.1666
900
10
1100
1E+16
3.355891
11,553.01
57.765043
6
750
N
Phosphorus
0.1666
900
1.91
1150
1E+16
2.599321
36,415.17
182.075865
6
750
Ramp Up Rate (°C/min)
Total Ramp Up Time (hrs)
Doping Process Time (hrs)
Total Ramp Down Time (hrs)
Total Process Time (hrs)
Total Furnace kW consumption
Total Support Equipment kW consumption
Total Clean room Cooling load due to equipment and
operators in kW
Total Controller and Base Level kW consumption
Total process kW consumption
Total kW consumption per wafer
Air handling unit, Air handler fan and Makeup fan kW
consumption
Total Fan filter unit kW consumption
Heat released from Fan filter units in kW
Additional kW consumption by the Clean room
Hence, Total Furnace kWh consumption per month
Total Support Equipment kWh consumption per month
Total Controller and Base Level kWh consumption per month
Total Clean room Cooling load due to equipment and
operators in kWh per month
Total process kWh consumption per month
Total kWh consumption per wafer
Air handling unit, Air handler fan and Makeup fan kWh
consumption per month
Total Fan filter unit kWh consumption per month
Heat released from Fan filter units per month
Additional kWh consumption by the Clean room
10
0.65
5
0.983333
6.633333
58.288836
30.882464
10
0.65
10
0.983333
11.633333
58.288836
30.882464
10
0.733333
1.91
1.047436
3.690769
60.099564
30.882464
5.148216
5.148216
5.238752
13.793018
108.112534
0.005336
13.793018
108.112534
0.009358
13.793018
110.013798
0.003021
12.46422
12.46422
12.46422
8.401306
6.656735
27.52226
10,531.31
5,011.55
9,268.91
8.401306
6.656735
27.52226
11,050.30
3,489.70
9,268.91
8.401306
6.656735
27.52226
9,867.21
7,834.58
9,268.91
1,240.59
1,190.45
1,348.54
26,052.36
1.286
24,999.36
2.164
28,319.24
0.778
8,375.96
8,375.96
8,375.96
5,645.68
4,473.33
18,494.96
5,645.68
4,473.33
18,494.96
5,645.68
4,473.33
18,494.96
Table 5.13: Results – N type dopant, drive-in time 1.91, 5, 10 hours, and temperature 1100 - 1150 °C [28]
Percentage energy savings per wafer = (1.28 kWh – 0.77 kWh)/1.28 kWh x 100
= 40 %/wafer
Referring Table 5.13, the throughput per month for a drive in time of 5 hours and 1.91 hours
per cycle is 20,261.31 and 36,415.17 wafers respectively.
Percentage excess wafers per month = (36,415 - 20,261)/20,261 x 100
= 80 %
88
5.3
Summary of major findings
•
Process temperature can be increased to obtain energy and productivity savings for
any given oxide thickness in oxidation process and for any dopant type in doping
process.
•
The increase in temperature yields higher energy savings for larger oxide thickness.
•
For smaller oxides to be grown, high temperature operation result in low energy
savings but high productivity improvement.
•
For a given set of parameters, increasing oxidation process temperature reduces the
energy (kWh) consumption per wafer.
•
For an oxide thickness of 1 micron, increasing temperature from 1000 °C to 1150 °C
could save 0.32 kWh/wafer (31 % per wafer). On an annual basis the energy saving
value of 516 MMBtu could be realized. Also, the facility can process approximately
66 % wafers per month in addition, because of the reduction in process time per
cycle.
•
For oxidation process, increasing the process temperature for a given set of
parameters reduces the total process time, which results in an increase in productivity.
•
An increase in ramp up rate, increases the power consumption of the furnace, but at
the same time reduces the ramp up time. Hence, the net effect of increasing ramp up
rate is the increase in energy (kWh) consumption per wafer, but more wafers could be
processed due to the reduction in ramp up time. An increase of 5 % in productivity is
realized for oxidizing wafers of 1 micron thickness but at the same time the energy
(kWh) per wafer increases by 10 %.
•
For a given drive – in process temperature, Phosphorus an N –type dopant will
achieve larger junction depth than Boron a P –type dopant because of its higher
diffusivity in silicon.
•
Larger junction depth values could be realized using Phosphorus an N –type dopant
than Boron a P –type dopant, for any given increase in process time, for a given set of
parameters.
•
For a junction depth of 2.45 microns using an N – type dopant, if the drive in
temperature is increased from 1100 °C to 1150 °C, the energy (kWh) requirement per
wafer reduces from 1.28 to 0.77 and the drive in time reduces from 5 hours to 1.91
89
hours per cycle. In other words, the percentage savings per wafer would be 40 % and
the percentage excess wafers per month processed would be approximately 80 %.
5.4
Conclusion
From the sensitivity analysis done for different parameters in oxidation and doping
process, it is found that there exists a feasible range over which these individual parameters
can be varied for a given set of other process parameters. This feasible range depends on the
desired specifications of the end product. It is also evident that process temperature is the
parameter governing energy in layering and diffusion process. Hence, varying the process
temperature within the practical limits will yield higher energy savings. The major energy
consuming elements being the process and the clean room equipment should be operated at
close to full utilization, in order to achieve reduced energy costs per wafer. This can be
achieved by increasing the throughput per month which is a function of the total process time
per cycle.. It was also found that varying process and production parameters does increase
production apart from the energy savings achieved. Reduction in energy requirement per
wafer could thus be achieved by increasing the utilization of the process equipment by
minimizing the process time. Various strategies to realize the mentioned facts are discussed
in detail.
90
Chapter 6
6 Model Validation
6.1
Validation of Model
In order to make the SECALPRO model more reliable and genuine, it becomes
necessary to have the study model built close to real time industrial scenario. To support this
argument, a two day visit to a leading research cum production facility in an adjacent state
was made [36]. The visit involved data gathering on process equipment, clean room
conditions, operating strategies, support equipment, power measurement on operating
process equipment, and discussions with the facility technical managers and staff. It was
found that, the facility had a similar kind of process equipment and production setup as found
in regular production facilities.
In the SECALPRO model built, the data for the Horizontal tube furnace and the
support equipment was collected from the leading equipment manufacturers. It was found
that similar kinds of equipment were used in the facility visited and also in the modern
semiconductor production facilities. Based on the data collected from the available literature,
publications and personal communication with the university faculty and lab managers, the
SECALPRO model was built. The SECALPRO model was then run for various process and
production parameters, and the results were exported to an Excel® spreadsheet. Different
graphs were plotted based on the results. Results obtained from the analysis were compared
with the real time power measurement done on a similar kind of Horizontal tube furnace at
the facility visited.
Table 6.1 shows the comparison between the furnace specifications used in the
SECALPRO model and the facility. It can be seen that both the furnace are four tube
horizontal stack type with a maximum kVA rating of 54 and 48 for the SECALPRO model
and facility furnace respectively [36]. The facility furnace can process a maximum of 100
wafers per cycle (25 wafers per boat x 4 boats) as compared to the SECALPRO model
furnace capacity of 200 wafers. Hence, the D. C. motors used for boat loading and unloading
operation in the SECALPRO model furnace are of each 35 W as compared to 10 W in the
facility furnace. Table 6.2 enlists the wafer cleaner specifications. Wafer cleaner equipment
rating used by the facility and SECALPRO model complies with each other.
91
Specification
Equipment
Manufacturer
Phase
KiloVolt – Amperes kVA
Power factor
No of tubes
Maximum wafers per boat
Pumps (hp and qty)
Boat loader (W and qty)
Boat loading temperature (°C)
24 x 7 operation (Y/N)
Idle temperature (°C)
Ramp up rate (°C / min)
Ramp down rate (°C / min)
SECALPRO model
Tube Furnace
TYSTAR
3 Φ, 3 wire
54
0.9
4
50
Pressurized gas cylinders
35 W, 4
750
Y
750
5
3
Facility
Tube Furnace
BRUCE
3 Φ, 3 wire
48
0.97
4
25
Pressurized gas cylinders
10 W, 4
800
Y
600
10
13, 8, 5
Table 6.1:Tube furnace specification [36]
Specification
Equipment
Phase
Voltage V
Current A
Power factor
SECALPRO model
Wafer cleaner
3 Φ, 3 wire
208
70
0.9
Facility
Wafer cleaner
3 Φ, 3 wire
208
60
0.97
Table 6.2: Wafer cleaner specification [36]
Item
Process
Wafer size (inches)
Oxide thickness (microns)
Oxidation process temperature (°C)
Boat loading temperature (°C)
Oxidation type
Oxide growth time (minutes)
SECALPRO
Facility
model
Oxidation
Oxidation
6
6
1
1
1000 - 1150
1100
750
800
wet
wet
134
152
Table 6.3: Sample oxidation process [36]
Item
SECALPRO
model
Doping
6
1100 - 1150
750
Boron, P
Process
Wafer size (inches)
Doping process temperature (°C)
Boat loading temperature (°C)
Dopant type
Table 6.4: Sample doping process [36]
92
Facility
Doping
6
1100
800
Boron, P
Table 6.3 and Table 6.4, compares a sample oxidation and doping process used in the
SECALPRO model and in the facility. Both the furnace can process wafers of six inch
diameter. The following parameters were compared and analyzed from the real time data,
viz. furnace energy consumption in kW at 750 °C, clean room design, and total oxide growth
time for a given set of parameters.
6.1.1
Furnace energy consumption
From the Figure 6.1 it is seen that the facility furnace [36] consumes around 18.8 kW
on average when ramping from 700 °C to 750 °C. The maximum power rating for that
furnace was 80 kW (√3 x 480 V x 100 A x 0.97/1000) as shown in Table 6.1. Hence, the
furnace consumes around 23 % of its maximum power. The furnace used in the SECALPRO
model has a maximum power rating of 84 kW (√3 x 54 kVA x 0.9) and consumes 18 kW (√3
x (11.2 + 12)/2 kVA x 0.9) when ramping from 700 °C to 750 °C, which is around 22 % of
its maximum power rating.
Ramp for 5 min 1:51 to 1:55 only - From 731C to 750C
40,000.00
35,000.00
30,000.00
kW
25,000.00
20,000.00
15,000.00
10,000.00
5,000.00
0.00
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59
Time
Figure 6.1: Power (kW) consumption vs. time – facility furnace [36]
Hence, it can be seen that the SECALPRO model closely represents the actual system.
This variation in percentage between 22% & 23% is due to the power factor for different
facilities. Figure 6.2 depict the power logger instrument connected to the control panel of the
facility furnace.
93
Figure 6.2: Power measurement using power logger [36]
6.1.2
Clean room design
Figure 6.3: Furnace loading/unloading station in Class 10 and reactor section in Class 1000 [36]
Specification
Clean room class 1
Clean room class 2
Clean room class 1, Volume (cubic.feet)
Clean room class 2, Volume (cubic.feet)
Clean room class 1, Air changes per hour
SECALPRO
model
Class 10
Class 1000
1,650
990
420
Facility
Class 10
Class 1000
4,800
1,500
300 - 400
Table 6.5: Clean room specification
Table 6.5 lists the clean room specifications. The facility has large clean rooms as compared
to the one used in the SECALPRO model. It can be seen that number of air changes per hour
for both the clean rooms are fairly close. The SECALPRO model assumes that the
furnace/reactor section is in a lower clean room class 1000 and the loading/unloading section,
94
where the wafers are exposed to the surrounding is class 10. The facility has a similar kind of
clean room classification used in the SECALPRO model as shown in Figure 6.3, Figure 6.4
and Table 6.5.
Class 10
Class 1000
Loading/unloading
Furnace/Reactor Section
Section +
Support equipment section
Figure 6.4: Clean room classification used in the SECALPRO model
HEPA filters similar to those used in the SECALPRO model are used in the facility clean
room to restrict the number of particles entering the clean room. After personal
communication and discussions with the facility personnel, it was found that around 100% of
the clean room ceiling area is covered by the HEPA filters for class 10, and around 90% 95% for class 100 and above [36]. The SECALPRO model considers 100 % HEPA ceiling
coverage.
6.1.3
Total oxide growth time
A sample recipe for the oxidation process as shown in Table 6.3 is considered for
comparison. As per the data acquired from the facility control station, a sample recipe for
growing 10,000 A (1 micron) thick oxides, <100> orientation, wet oxidation, at 1100 °C
needs around 152 minutes (2.5 hours). This time represents the time required to grow the
oxide per cycle. After running the SECALPRO model for the parameters mentioned above, it
revealed that the furnace used in the model requires around 134 minutes (2.2 hours) which is
close to the collected data. The reason for the difference in time may be due to the variation
in coil heater arrangement and tube material, for the different furnace which will need
different time to stabilize at a constant temperature.
95
6.2
Important facts revealed after personal communication
The facility has personnel from sound industrial, research and academic background
[36]. Personal discussions with some of them revealed important facts. These facts support
the assumptions used in the SECALPRO model.
1. In industries/production facilities, not much importance is given to energy. Only
successful production is carried out at the expense of energy.
2. Most of the processes are temperature and time dependent. The industries have a set
of recipes to suit individual jobs.
3. Generally, the time required growing the oxide in case of oxidation and the time
required to achieve the desired junction depth in case of doping is increased to realize
higher values. But, temperature is not changed much. There are no specific reasons to
not increasing the process temperature in the feasible range.
4. Also, increasing the process temperature was never thought of as a means to
accelerate the process, minimize the process time, and hence reduce energy
consumption.
5. Maximum surface concentration of 1 E16 atoms/cm3 and 0.18 Ώ resistance for doping
process are reasonable values to use.
6. A feasible or permissible temperature range to vary may be 100 – 150 °C.
7. Diffusion process is used for doping because it accelerates the process and deeper
junction depths can be achieved in considerable time. “Ion implantation” is only
suitable for shallow depths.
6.3
Conclusion
The model furnace consumes approximately 22 % of its maximum power at 750 °C,
which is the idle temperature for the SECALPRO model furnace under study. To minimize
the energy requirement per wafer, the furnace should be cycled for a maximum throughput
per month. This can be achieved by the reduction in process time per cycle and thus
maximizing number of cycles per month. The clean room cubic feet volume for the facility
module is large enough as compared to the SECALPRO model clean room. Hence, the clean
room class energy requirement would be even higher for the facility furnace. Similar ceiling
coverage using fan filter units (FFUs) was observed at the facility [36]. From the above
analysis, comparisons and discussions the SECALPRO model is said to be closely
96
representing the actual system. Hence, the SECALPRO model could be used to estimate the
energy requirement of energy intensive processes such as oxidation and doping in
semiconductor manufacturing.
97
Chapter 7
7 Conclusion and Future Work
7.1
Conclusion
The making of a microchip from raw silicon to assembled package is a complex
process. It involves a large number of processes. Due to the process complexity and low
yield, enough attention is given to the process. But, energy consumed to perform these
processes is somewhat sideline. Energy conservative policies implemented in the
semiconductor industry were limited to the more distinctive and lucrative HVAC
opportunities. Equally important and significant candidate for energy conservation are the
process equipment. There exists an opportunity to optimize the energy used by the process
equipment. To arrive at some specific energy conservative policies and operating strategies,
it is first necessary to estimate the energy used by the process equipment to carry out these
individual processes.
Real time energy estimation requires the use of power logger equipment and an
electrician to carry out this activity. Also, it should be done by a trained personnel or
electrician which would otherwise be an unsafe practice. The research focused on developing
a SECALPRO model capable of estimating the energy utilization by the energy intensive
processes in semiconductor manufacturing. The SECALPRO model being a computer
program is free from the previously mentioned lacunas of physical energy measurement.
Data is collected from appropriate sources; the model is then built and validated. It can be
used by the process designers and production managers together before the production starts,
and can arrive at a set of process parameters governing and optimizing the process energy
use.
From the analysis done for different parameters governing the process energy it can
be seen that, it is extremely important for the semiconductor fabrication units to develop
operating strategies that allow energy efficient operation. The SECALPRO model can be
used by the system analyst easily to identify the effect of production parameters on energy.
As the use of SECALPRO model does not hamper the production process, the user may run
the model for a set of operating conditions and study the results. The results obtained from
98
the analysis will enable the management and the plant managers to modify the plant
strategies regarding production.
The specific objectives of this research sought have been accomplished and are
further discussed.
¾
Energy utilization and analysis in semiconductor manufacturing
Figure 7.1 depicts the total energy usage for the oxidation process carried out in a
clean room to grow a 1 micron thick oxide at 1150 °C. From Figure 7.1 it is seen that the
major energy consuming elements in a semiconductor manufacturing process such as
oxidation are the Process and Support equipment, Clean room cooling load due to equipment
and operators, Clean room energy use by Air handling system, Fan Filter unit, and the heat
released from Fan Filter unit. Energy usage for a process conducted in a clean room can be
categorized into fixed and variable usage.
Heat from Fan
Filter unit
10%
Furnace
20%
Fan Filter unit
12%
Air Handling
System
18%
Clean room cooling
load
3%
Support Equipment
18%
Controller and Base
level
19%
Figure 7.1: Energy usage distribution – 1 micron oxide, 1150 °C
The energy requirement for each of the fixed and variable energy elements is given.
Fixed Energy requirement:
a)
Air handling system
18 %
99
b)
Fan Filter unit
12 %
c)
Heat released from Fan Filter unit
10 %
40 %
Variable Energy requirement:
a)
Process and Support equipment
38 %
b)
Clean room cooling load due to equipment and operators
3%
c)
Controller and Base level operation
19 %
60 %
Hence, approximately 60 % of the total energy used in a clean room semiconductor
manufacturing process such as oxidation will vary based on the process parameters. Also,
approximately 40 % of the total energy accounts for the fixed elements. It is important to
notice that the results given above pertaining to oxidation process involve use of energy
intensive equipment like furnace. Hence, the fixed energy requirement (clean room mainly)
is lower than variable energy (process equipment mainly) requirement. On a larger
perspective, there are other processes in semiconductor manufacturing which require less
energy intensive equipment but equivalent fixed energy in terms of clean room. Therefore, in
totality, the average fixed energy and variable energy requirement of complete
semiconductor manufacturing will differ from the one reported before.
¾
Sensitivity of production parameters with respect to energy consumption.
Layering/Oxidation
Sensitivity of process parameters with respect to energy revealed that process
temperature is the parameter most sensitive to the process energy. By increasing the
oxidation process temperature from 1000 °C to 1150 °C, when growing 1 micron thick oxide
on a bare silicon wafer surface can result in the following maximum energy savings and %
productivity increase.
Energy kWh savings per wafer
= 0.324 kWh/wafer
Percentage energy savings per wafer
= 30.59 %
Percentage excess wafers processed per month
= 65.9 %
100
Diffusion/Doping
From the analysis done for doping process it was found that process temperature and
dopant type are the parameters most sensitive to the process energy. For the given set of
process parameters, Phosphorus – N type dopant will form higher junction depths as
compared to Boron – P type dopant. Increasing the process temperature by 50 °C when
diffusing dopants into the wafer surface will minimize the doping process time per cycle as
well reduce energy consumption per wafer. Figure 7.2 depicts the total energy usage per
cycle for the doping process carried out in a clean room for N type dopant - Phosphorus at
1150 °C.
Heat from Fan
Filter unit
10%
Furnace
23%
Fan Filter unit
13%
Support Equipment
11%
Air Handling
System
19%
Clean room cooling
load
3%
Controller and Base
level
21%
Figure 7.2:Energy usage distribution – N type dopant phosphorus, 1150 °C
Hence, the resulting maximum energy savings and % productivity increase due to increase in
drive in process temperature from 1100 °C to 1150 °C for N type dopant - Phosphorus would
be;
Energy savings per wafer
= 0.51 kWh/wafer
Percentage energy savings per wafer
= 40 %wafer
101
Percentage excess wafers per month
¾
= 80 %
Verification and Validation of the designed SECALPRO model
The model was validated on the basis of the following two aspects:
1. Use of process parameters from a semiconductor production facility to estimate the
total energy requirement [36]. It was revealed that the energy results obtained by
running the SECALPRO model using the facility data closely represent to the real
time power measurement on the facility equipment.
2. The SECALPRO model is also validated based on the clean room design, type of
equipment used.
7.2
Future work
The research has covered the objective of performing initial evaluation of process
energy requirement and conservation for two different energy intensive processes in
semiconductor manufacturing. Also, the SECALPRO model can be used for analysis using
more realistic industrial data. The SECALPRO model is ideal for analyzing the effect of
selected process and production parameters on energy usage for a particular energy intensive
process. Hence, the user is aware of the effects of change in energy usage due to change in
process/production parameters prior to its implementation. Real time industrial data was used
to evaluate the SECALPRO model. It becomes necessary to collect data for a particular
process from several production facilities and then use it in the SECALPRO model. This will
make the model more robust and could then determine the reason for varying process
equipment energy consumption for different production facilities. But a considerable amount
of effort would be required to gather data regarding the input parameters for a particular
process from various facilities.
7.2.1
Linear programming approach
Although, based on the results from the analysis done in previous chapters, certain
strategies regarding process optimality can be developed. After running the SECALPRO
model several times, the user can finalize a process recipe for a given set of input parameters
to minimize the total energy usage. Further development to the SECALPRO model would be
102
the use of linear programming approach to arrive at an optimal solution. Hence, a brief
introduction to the approach is given here.
Let,
The total energy used per production cycle be
=y
Furnace energy consumption per cycle
= y1
Support equipment (Wafer Handler) energy consumption per cycle
= y2
Support equipment (Wafer Cleaner) energy consumption per cycle
= y3
Support equipment (Wafer Scrubber) energy consumption per cycle
= y4
Base level and controller energy consumption per cycle
= y5
Cooling load due to equipment, operators, and FFU
= y6
Therefore,
y = function (y1, y2, y3, y4, y5, y6)
(7.1)
Now, from equation 7.1, it is clear that, the total energy used per production cycle is a
function of several different processes which combine together to form a complete cycle.
Now, it becomes necessary to break up this total energy consumption (y) into individual
process energy consumption to study their effect.
Furnace energy
The Figure 7.3, Temperature vs. time – Furnace cycle relationship is again presented here for
ready reference. As can be seen in the Figure 7.3, energy is used for the following sub
processes by the elements within the furnace:
Let,
Ramp up energy consumption per cycle
= a1
Process energy consumption per cycle
= a2
Ramp down energy consumption per cycle
= a3
a1 = function (Ramp up rate, Idle temperature, Process temperature)
103
a2 = function (type, orientation, oxide thickness, process temperature, drive-in time, junction
depth, drive-in temperature)
a3 = function (Ramp down rate, Idle temperature, Process temperature)
Therefore,
Temperature (ºC)
y1 = function (a1, a2, a3)
(7.2)
Process
Ramp down
Ramp up
Boat loading
Boat unloading
Time (hrs)
Figure 7.3: Temperature vs. time – Furnace cycle
Support equipment (Wafer Handler)
y2 = function (wafers processed per cycle, wafer size, boat loading/unloading speed)
Support equipment (Wafer Cleaner)
y3 = function (wafers processed per hour, furnace throughput per cycle, utilization factor)
Support equipment (Wafer Scrubber)
y4 = function (furnace throughput per cycle, utilization factor)
Base and Controller
y5 = function (Idle temperature, controller kVA, facility operating hours)
Cooling load due to equipment, operators, and FFU
y6 = function (y1, y2, y3, y4, y5, # of operators, insulation)
104
Based on the above parameters governing energy for individual processes, the total energy
consumption per production cycle can be minimized.
The following work needs to be performed on the SECALPRO model
1. Fine tune the model so that it accurately measures the system energy.
2. Incorporate heat losses precisely from the equipment in a clean room by applying
Heat transfer concepts.
3. Form a database of the information obtained from the analysis, which can be further
used as a knowledge base.
4. Run the model for the data gathered on process/production parameters for a particular
process from several manufacturing facilities.
5. Execute the model with the varying inputs from the data gathered and analyze the
output to develop operating strategies for the production process.
6. Expand the model for the remaining non energy intensive processes.
7. Apply Linear programming approach to optimize the process energy requirement.
105
Reference
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Cost Savings and Pollution Prevention”, Semiconductor Fabtech, Edition 8, 1997.
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Manual”, Prentice Hall, 2001.
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Chapter 3, Crystal Growth and Wafer Preparation, pp 45 – 62 Second Edition,
Mcgraw-Hill, Inc, 1990
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“http://sst.pennnet.com/Articles”.
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in
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1998,
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Fabrication
Energy
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the Association of Energy Engineers, Volume 94, No 2, pp 58 -79, 1997
106
14. Dunning. S, Segee. B, Allen. V, “A Self-Assessment Software Applicationfor
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1998.
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Fifth Annual IEEE Applied Power Electronics Conference and Exposition - APEC
'90, Los Angeles, CA, USA, March 1990.
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IEEE, Volume 86, No 1, pp 176-183, Jan 1998.
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Chemical Vapor Deposition (CVD) Equipment”, pp 48-51, IEEE/SEMI Advanced
Semiconductor Manufacturing Conference, 2004
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chapter 3, pp 47 - 48, second edition, McGraw-Hill, Inc, 1990.
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Silicon, www.merc.iastate.edu”, 2000.
107
27. Zant. P, “Microchip Fabrication – A Practical Guide to Semiconductor Processing”
Chapter 11, pp 259 - 296, Second Edition, Mcgraw-Hill, Inc, 1990.
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Diffusion in Silicon, Lattice Press California, 2004.
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Volume 10, No. 3a, 2002.
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http://tuttle.merc.iastate.edu/ee432/notes/diffusion/diffusion.pdf, 2004
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Initiative, www. nwalliance.org “, 2004.
108
Nomenclature
Variable
kW
h
N
Si
O2
SiO2
H2O
2H2
Ji
JS i O2
J
D
Ea
k
Ni
No
t ox
ks
k so
t
GR
M
B
A
B/A
τ
C ( x, t )
F
C
Cs
erfc
Q0
x
Csub
G
Description
Average Electric Power
Wafer processing time in hours
Number of wafers processed during the time period
Silicon source from wafer
Oxygen gas (dry oxidation)
Silicon dioxide layer
Water vapor (wet oxidation)
Hydrogen (product of chemical reaction)
Flux of oxidizing elements reaching the interface
flux consumed in the reaction
flux of the diffusing molecules
Diffusion coefficient / diffusivity
Activation energy
Gas constant
The concentration of oxidizing molecules at the interface Ni
The concentration of oxidizing element molecules
Oxide thickness to be grown
Reaction rate
concentration of oxidizing elements at the interface
Oxide growth time
Growth Rate of the SiO2
Atoms per cubic cm
Constant, parabolic Growth rate
Constant
linear growth rate
Time required to grow oxide already present on wafer
Number of particles per cubic cm at position x, at time t
Number of particles per square cm-sec
Impurity concentration
Surface concentration of dopant
Complementary error function
fixed dopant dose
Junction depth
Substrate concentration
Parabolic growth rate (B) or Linear growth rate (B/A)
Table N.1: Nomenclature for equations
109
Equation
number
2.1
2.1
2.1
3.1
3.1
3.1, 3.2
3.2
3.2
3.3
3.3
3.4
3.4
3.5
3.5
3.6, 3.7
3.6
3.6
3.8
3.8
3.9
3.9, 3.10
3.11
3.16
3.17
3.19
3.19
3.20
3.20
3.23
3.26
3.28
3.28
3.29
4.1
Appendix I
SECALPRO Model Screenshots
Figure I.1: Screenshot of the SECALPRO model - Introduction
Figure I. 2: Screenshot of the SECALPRO model - Execution
110
Figure I.3: Screenshot of the SECALPRO model - Execution
Figure I.4: Screenshot of the SECALPRO model – Results (1 micron, 1150 °C, wet, <100>)
111
Appendix II
List of variables
SECALPRO Model Variables
Variable
tox
type
orientation
GoB
EaB
GoBA
EaBA
OxTempC
G1
G2
k1
k2
k
D1
thp
C
IdleTemp
RUR
RT2
RT3
RT
RD1
RD2
RD3
RD4
TRD
TPT
NER
RTkW2
kWBLM
RTkW3
Type
char
char
char
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
kW
RDkW4
RDkW5
kWBUM
RTkWh2
kWhBLM
RTkWh3
double
double
double
double
double
double
double
Description
Oxide thickness to be grown
Oxidation type
Wafer orientation
Parabolic growth diffusion co efficient
Parabolic growth activation energy
Linear growth diffusion co efficient
Linear growth activation energy
Oxidation process temperature
Parabolic growth diffusivity
Linear growth diffusivity
Parabolic growth time
Linear growth time
Total oxidation time
Wafer diameter
Wafer throughput per month
Oxidation process cycles per month
Idle temperature of the furnace
Ramp up rate
Wafer boat loading time
Ramp up time from IdleTemp to OxTempC
Total ramp up time
Ramp down time from OxTempC to 1100 °C
Ramp down time from 1100 °C to 900 °C
Ramp down time from 900 °C to IdleTemp
Wafer boat unloading time
Total ramp down time
Total process time per oxidation cycle
Number of equipment required
Power consumption during boat loading
Power consumption by boat loader motor
Power consumption from IdleTemp to OxTempC temperature
rise
Power consumption during oxide growth
Power consumption from OxTempC to IdleTemp
Power consumption during boat unloading
Power consumption by boat unloader motor
Energy consumption during wafer boat loading
Energy consumption by wafer boat loader motor
Energy consumption from IdleTemp to OxTempC
112
kWh
RDkWh4
RDkWh5
kWhBUM
kWEFans
kWhEFans
kWT
kWhT
CkW
CT
CkWh
RT1
RTkW1
BLOkW
BLOT
BLOkWh
Vol1
Mass1
Vol2
Mass2
M
HP
kW1
kWh1
V2
A2
kW2
CUF
kWh2
V3
A3
kW3
kWh3
Cl
Volume1
FFU1
C2
Volume2
FFU2
FFU
FE/kW4
kWh4
Carea
AHkW
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
double
temperature rise
Energy consumption during oxide growth
Energy consumption from OxTempC to IdleTemp
Energy consumption during wafer boat unloading
Energy consumption by wafer boat unloader motor
Power consumption by furnace exhaust and scavenger fans
Energy consumption by furnace exhaust and scavenger fans
Total power consumption of furnace
Total energy consumption of furnace per cycle
Furnace controller power consumption
Controller operation time per month
Furnace controller energy consumption per month
Ramp up time required from 400 °C to IdleTemp
Furnace power from 400 °C to IdleTemp
Base level operation furnace power consumption
Base level operation time per month
Base level operation furnace energy consumption
Total volume of wafers in a cassette
Total mass of wafers in a cassette
Total volume of cassette
Total mass of cassette
Total mass to be lifted by wafer handler
Wafer handler motor horse power requirement
Wafer handler power consumption
Wafer handler consumption per cycle
Voltage rating for wafer cleaner
Amperage of wafer cleaner
Wafer cleaner power requirement
Wafer cleaner utilization per oxidation cycle
Energy consumption by wafer cleaner per oxidation cycle
Voltage rating for wafer scrubber
Amperage of wafer cleaner scrubber
Wafer scrubber power requirement
Energy consumption by wafer scrubber per oxidation cycle
Clean room 1 class desired
Clean room 1 volume requirement
Fan filter unit requirement for clean room 1
Clean room 2 class desired
Clean room 2 volume requirement
Fan filter unit requirement for clean room 2
Total Fan Filter Unit requirement
Total fan filter unit power requirement
Total energy consumption by fan filter unit per month
Clean room ceiling area
Total air handler unit power requirement
113
AHFkW
MUFkW
AHkWh
AHFkWh
MUFkWh
HEkW
HEkWh
HOkW
HOkWh
HFFUkW
HFFUkWh
dopant
do2
Cx2
Csub
deptime
deptemp
drintime
drintemp
Qo
double
double
double
double
double
double
double
double
double
double
double
Char
double
double
double
double
double
double
double
double
Xj
double
Total air handler fan power requirement
Total makeup fan unit power requirement
Total air handler unit energy consumption per month
Total air handler fan energy consumption per month
Total makeup fan unit energy consumption per month
Total heat load from equipment per cycle
Total energy consumption due to equipment heat load
Total heat load due to operators per cycle
Total energy consumption due to operator heat load
Total heat load from fan filter units
Total energy consumption due to fan filter unit heat load
Type of dopant desired
Drive-in process diffusion coefficient
Surface concentration
Substrate concentration
Pre deposition step time
Pre deposition step temperature
Drive-in step time
Drive-in step temperature
Number of dopant atoms introduced during pre deposition
step
Junction depth
Table AII.1: Variable data type and description
114
Appendix III
Source code
# include<iostream.h>
# include <stdio.h>
# include<conio.h>
# include<iomanip.h>
# include<math.h>
#include <dos.h>
void main()
{
double thp,C,x1,x2,x3,x4,RUR,CUF,Ctime,WCTP;
double kWmax,kW,kWh,kW1,kWh1;
double RT,RD1,RD2,RD3,OxTempC,OxTempK,IdleTemp,IdT1,IdT2,IdT3,IdT4;
double RD,TRD,RD4,RD5,RT1,RT2,RT3,TPT,BLOkW,BLOT,BLOkWh;
double k,kWEFans,kWhEFans,HOkW,HOkWh, RTkW1max,RTkW1,RTkW2max;
double RDkW5,RDkW6,RTkWh2,RTkWh3,RDkWh4,RDkWh5;
double RTkW2,RTkW3max,RTkW3,RDkW4,RDkW5max;
double CT,CkW,CkWh,kWBUM,kWhBUM,kWBLM,kWhBLM;
double y1,y2,y3,y4,y5,y6,y7,y8,y9;
double kW2,V2,A2,kWh2,kW3,V3,A3,kWh3,kW4,kWh4;
double D1,v1,Vol1,n,Mass1,L2,Vol2,Mass2,M,W,HP,T1,HEkW,HEkWh,kWT,kWhT;
double Cl,C2,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10;
double tox,GoB,EaB,GoBA,EaBA,G1,G2,k1,k2,Volume1,Volume2;
double FFU,FFU1,FFU2,HFFUkW,HFFUkWh,FE;
double R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,R11,R12,R13,R14,R15,R16,R17,R18,R19,R20;
char type,orientation,dopant,exit;
int number;
double drintime,drintemp,drintempk,dexp2,d2,Cx2,Csub,Qo,Q2,X,X1,X2,Xj;
double deptime,deptemp,deptempk,dexp1,do2,d1,Ea;
double AHUnits,AHFan,MUFan,AHkW,AHkWh,AHFkW,AHFkWh,MUFkW,MUFkWh;
double Carea,Carea1,Carea2,Carea3,Carea4,Carea5;
printf("\n\n\n");
printf("\t \tThis Program enables the user to estimate the energy used in \n \t\t\t\tSemiconductor
Manufacturing");
cout<<"\n";
printf("");
getch();
cout<<"\n";
cout<<"\n";
cout<<"\t The energy intensive processes in semiconductor manufacturing are";
cout<<"\n\t \t";
cout<<"\n\t \t 1.Ingot formation";
cout<<"\n\t \t 2.Oxidation/Diffusion";
cout<<"\n\t \t 3.Doping";
cout<<"\n\t \t 4.Heat treatment";
cout<<"\n";
cout<<"\n\t Layering and Diffusion have more degrees of freedom regarding energy";
cout<<"\n";
cout<<"\n\t Please select the desired process by selecting the number in brackets";
cout<<"\n";
cout<<"\n\t Layering/Oxidation (1)";
cout<<"\n\t Diffusion/Doping (2)";
115
cout<<"\n";
cout<<"\n \t Enter a number to select the process \n";
cout<<"\n \t ";
cin>>number;
switch (number)
{
case 1:
cout<<"\n";
cout<<"\t \t \t Operation is Oxidation \n";
cout<<"\n";
cout<<"\n \t Enter Thickness of Oxide Film in microns (u) \n";
cout<<"\n \t ";
cin>>tox;
cout<<"\n";
cout<<"\n \t Enter type of Oxidation ";
cout<<"\n \t Wet - w or Dry - d";
cout<<"\n";
cout<<"\n \t ";
cin>>type;
cout<<"\n";
cout<<"\n \t <100> orientation is although widely used, the user can";
cout<<"\n \t select either of <100> and <111> type";
cout<<"\n \t Enter type of orientation ";
cout<<"\n \t 100 - o or 111 - i ";
cout<<"\n";
cout<<"\n \t ";
cin>>orientation;
cout<<"\n";
if ((type == 119) && (orientation == 111))
{
GoB = 386;
EaB = 0.78;
GoBA = 97000000;
EaBA = 2.05;
}
if ((type == 119) && (orientation == 105))
{
GoB = 386;
EaB = 0.78;
GoBA = 163000000;
EaBA = 2.05;
}
if ((type == 100) && (orientation == 111))
{
GoB = 772;
EaB = 1.23;
GoBA = 3700000;
EaBA = 2;
}
if ((type == 100) && (orientation == 105))
{
GoB = 772;
EaB = 1.23;
GoBA = 6230000;
EaBA = 2;
}
116
//cout<<"\n \t type = "<<type;
//cout<<"\n \t orientation = "<<orientation;
//cout<<"\n \t GoB ="<<GoB;
//cout<<"\n \t EaB ="<<EaB;
//cout<<"\n \t GoBA ="<<GoBA;
//cout<<"\n \t EaBA ="<<EaBA;
cout<<"\n \t Specify the Oxidation Temperature in Degree Celsius (C)\n";
cout<<"\n \t ";
cin>>OxTempC;
cout<<"\n";
OxTempK = OxTempC + 273;
G1 = GoB*(exp(-EaB/0.00008625/OxTempK));
G2 = GoBA*(exp(-EaBA/0.00008625/OxTempK));
//cout<<"\n \t G1 = "<<G1;
//cout<<"\n \t G2 = "<<G2;
k1 = (tox*tox)/G1;
k2 = (tox)/G2;
//cout<<"\n \t k1 = "<<k1;
//cout<<"\n \t k2 = "<<k2;
k = k1 + k2;
//cout<<"\n \t k = "<<k;
cout<<"\n \t Enter Wafer diameter in inches \n";
cout<<"\n \t ";
cin>>D1;
cout<<"\n";
cout<<"\n \t Enter Idle Temperature of Furnace/Oxidizer";
cout<<"\n \t";
cout<<"\n \t ";
cin>>IdleTemp;
cout<<"\n \t";
cout<<"\n \t";
cout<<"\n \t Specify the Ramp Up Rate per minute desired in degree Celsius";
cout<<"\n \t";
cout<<"\n \t ";
cin>>RUR;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermining Ramp Up Time in hours";
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermining RT2, Wafer Boat Loading time";
cout<<"\n \tLoading Speed is 15 inch per minute";
RT2 = (60)/15;
cout<<"\n \t RT2="<<RT2/60;
cout<<"\n";
cout<<"\n \tDetermining RT3, Range IdleTemp to OxTempC";
RT3 = (OxTempC - IdleTemp)/RUR;
cout<<"\n \t RT3="<<RT3/60;
cout<<"\n \t";
RT=(RT2+RT3)/60;
cout<<"\n";
cout<<"\n \tTotal Ramp Up Time in hours RT="<<RT;
cout<<"\n";
cout<<"\n";
117
cout<<"\n";
cout<<"\n \tOxidation Process Time="<<k;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermining Ramp Down Time in hours";
cout<<"\n \t";
cout<<"\n \t";
cout<<"\n \tDetermining Ramping Down Time from OxTempC to IdleTemp in hours";
x1=1300;
x2=1100;
x3=900;
x4=750;
if (OxTempC <= x1 && OxTempC > x2)
{
RD1 = (OxTempC - 1100)/13;
RD2 = (1100 - 900)/8;
RD3 = (900 - IdleTemp)/5;
cout<<"\n";
cout<<"\tRD1="<<RD1/60;
cout<<"\n";
cout<<"\tRD2="<<RD2/60;
cout<<"\n";
cout<<"\tRD3="<<RD3/60;
cout<<"\n";
RD=(RD1+RD2+RD3)/60;
cout<<"\tRamp Down Time in hours RD="<<RD;
cout<<"\n";
}
if (OxTempC <= x2 && OxTempC > x3)
{
RD1 = (OxTempC - 900)/8;
RD2 = (900 - IdleTemp)/5;
cout<<"\n \tRamp Down Time per cycle in hours";
cout<<"\n";
cout<<"\tRD1="<<RD1/60;
cout<<"\n";
cout<<"\tRD2="<<RD2/60;
cout<<"\n";
RD=(RD1+RD2)/60;
cout<<"\tRamp Down Time in hours RD="<<RD;
cout<<"\n";
}
if (OxTempC <= x3 && OxTempC > x4)
{
RD1 = (OxTempC - IdleTemp)/5;
cout<<"\n \tRamp Down Time per cycle in hours";
cout<<"\n";
cout<<"\n";
RD=(RD1)/60;
cout<<"\tRamp Down Time in hours RD="<<RD;
cout<<"\n";
}
cout<<"\n \tDetermining RD4, Wafer Boat Unloading time";
cout<<"\n \tUnloading Speed is 15 inch per minute";
RD4 = (60)/15;
118
cout<<"\n \tRD4="<<RD4/60;
cout<<"\n";
cout<<"\n";
cout<<"\n";
TRD=RD+(RD4)/60;
cout<<"\tTotal Ramp Down Time in hours TRD="<<TRD;
cout<<"\n";
cout<<"\n";
cout<<"\n";
TPT= (RT+k+TRD);
cout<<"\n \tTotal Process Time in hours ="<<TPT;
cout<<"\n \tFor a 24 x 7 operation, the throughput per month";
cout<<"\n \tis estimated as follows";
C = (24*7*4)/TPT;
cout<<"\n \tOxidation cycles per month";
cout<<"\n \tC = "<<C;
cout<<"\n";
cout<<"\n";
thp = C * 200;
cout<<"\n \t";
cout<<"\n \tThroughput - Wafers processed per month = "<<thp;
cout<<"\n \t";
cout<<"\n";
cout<<"\n \tDetermining Power(kW) consumption";
IdT1=600;
IdT2=650;
IdT3=700;
IdT4=750;
// For Idle temperature of 600 C
if (IdleTemp == IdT1)
{
RTkW2max = 1.732*0.9*8.8;
}
// For Idle temperature of 650 C
if (IdleTemp == IdT2)
{
RTkW2max = 1.732*0.9*10;
}
// For Idle temperature of 700 C
if (IdleTemp == IdT3)
{
RTkW2max = 1.732*0.9*11.2;
}
// For Idle temperature of 750 C
if (IdleTemp == IdT4)
{
RTkW2max = 1.732*0.9*12;
}
cout<<"\n \tBoat Loading is done at IdleTemp. Hence, the";
cout<<"\n \tKilowatt consumed during Boat Loading";
RTkW2 = RTkW2max*RUR/22;
cout<<"\n \t="<<RTkW2;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tSmall motors of 0.5 hp are used for Boat Loading operation.";
119
cout<<"\n \tFor each of the 4 boats, a sepaerate motor is used";
kWBLM = (4*0.5*0.746);
cout<<"\n \tkilowatt for Boat Loader motor is ="<<kWBLM;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermine Kilowatt consumed from IdleTemp to OxTempC";
IdT1=600;
IdT2=650;
IdT3=700;
IdT4=750;
y1=900;
y2=950;
y3=1000;
y4=1050;
y5=1100;
y6=1150;
y7=1200;
y8=1250;
y9=1300;
// For Idle temperature of 600 C
if (OxTempC == y1 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16)/7;
}
if (OxTempC == y2 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18)/8;
}
if (OxTempC == y3 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6)/9;
}
if (OxTempC == y4 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22)/10;
}
if (OxTempC == y5 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4)/11;
}
if (OxTempC == y6 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26)/12;
}
if (OxTempC == y7 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/13;
}
if (OxTempC == y8 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/14;
}
if (OxTempC == y9 && IdleTemp == IdT1)
{
120
RTkW3max =
1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/15;
}
// For Idle temperature of 650 C
if (OxTempC == y1 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16)/6;
}
if (OxTempC == y2 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18)/7;
}
if (OxTempC == y3 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6)/8;
}
if (OxTempC == y4 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22)/9;
}
if (OxTempC == y5 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4)/10;
}
if (OxTempC == y6 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26)/11;
}
if (OxTempC == y7 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/12;
}
if (OxTempC == y8 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/13;
}
if (OxTempC == y9 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/14;
}
// For Idle temperature of 700 C
if (OxTempC == y1 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16)/5;
}
if (OxTempC == y2 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18)/6;
}
if (OxTempC == y3 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6)/7;
}
if (OxTempC == y4 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22)/8;
121
}
if (OxTempC == y5 && IdleTemp == IdT3)
{
RTkW3max = (1.732*0.9)*(11.2+12+12.8+14.4+16+18+19.6+22+24.4)/9;
}
if (OxTempC == y6 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26)/10;
}
if (OxTempC == y7 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/11;
}
if (OxTempC == y8 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/12;
}
if (OxTempC == y9 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/13;
}
// For Idle temperature of 750 C
if (OxTempC == y1 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16)/4;
}
if (OxTempC == y2 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18)/5;
}
if (OxTempC == y3 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6)/6;
}
if (OxTempC == y4 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22)/7;
}
if (OxTempC == y5 && IdleTemp == IdT4)
{
RTkW3max = (1.732*0.9)*(12+12.8+14.4+16+18+19.6+22+24.4)/8;
}
if (OxTempC == y6 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26)/9;
}
if (OxTempC == y7 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/10;
}
if (OxTempC == y8 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/11;
}
if (OxTempC == y9 && IdleTemp == IdT4)
122
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/12;
}
RTkW3 = (RTkW3max*RUR)/22;
cout<<"\n \tKilowatt consumed from IdleTemp to OxTempC="<<RTkW3;
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermine Kilowatt consumed for Process at OxTempC";
y1=900;
y2=950;
y3=1000;
y4=1050;
y5=1100;
y6=1150;
y7=1200;
y8=1250;
y9=1300;
if (OxTempC == y1)
{
kWmax = 1.732*0.9*(16);
}
if (OxTempC == y2)
{
kWmax = 1.732*0.9*(18);
}
if (OxTempC == y3)
{
kWmax = 1.732*0.9*(19.6);
}
if (OxTempC == y4)
{
kWmax = 1.732*0.9*(22);
}
if (OxTempC == y5)
{
kWmax = 1.732*0.9*(24.4);
}
if (OxTempC == y6)
{
kWmax = 1.732*0.9*(26);
}
if (OxTempC == y7)
{
kWmax = 1.732*0.9*(28.8);
}
if (OxTempC == y8)
{
kWmax = 1.732*0.9*(32.8);
}
if (OxTempC == y9)
{
kWmax = 1.732*0.9*(35.2);
}
kW=kWmax*RUR/22;
cout<<"\n \tKilowatt consumed for Process at OxTempC="<<kW;
cout<<"\n";
123
cout<<"\n";
// kilowatt consumed during ramp down will be approximately equal to that consumed at idle temp
cout<<"\n \tDetermine Kilowatt consumed from OxTempC to IdleTemp";
RDkW4=RTkW2;
cout<<"\n \tKilowatt consumed from OxTempC to IdleTemp ="<<RDkW4;
IdT1=600;
IdT2=650;
IdT3=700;
IdT4=750;
// For Idle temperature of 600 C
if (IdleTemp == IdT1)
{
RDkW5max = 1.732*0.9*8.8;
}
// For Idle temperature of 650 C
if (IdleTemp == IdT2)
{
RDkW5max = 1.732*0.9*10;
}
// For Idle temperature of 700 C
if (IdleTemp == IdT3)
{
RDkW5max = 1.732*0.9*11.2;
}
// For Idle temperature of 750 C
if (IdleTemp == IdT4)
{
RDkW5max = 1.732*0.9*12;
}
cout<<"\n";
cout<<"\n";
cout<<"\n \tBoat Unloading is done at IdleTemp. Hence, the";
cout<<"\n \tKilowatt consumed during Boat Unloading";
RDkW5= RDkW5max*RUR/22;
cout<<"\n \t="<<RDkW5;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tSmall motors of 0.5 hp are used for Boat Unloading operation.";
cout<<"\n \tFor each of the 4 boats, a sepaerate motor is used";
kWBUM = (4*0.5*0.746);
cout<<"\n \tkilowatt for Boat Unloader motor is ="<<kWBUM;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tNow determining the Process Energy as a function of time i.e. kWh";
cout<<"\n";
cout<<"\n";
cout<<"\n";
RTkWh2=RTkW2*RT2/60;
cout<<"\n \tKilowatt-hour consumed for Wafer Boat Loading="<<RTkWh2;
kWhBLM = kWBLM*(RD4/60);
cout<<"\n \tkilowatt-hour for Boat Loader motor is ="<<kWhBLM;
cout<<"\n";
RTkWh3=RTkW3*RT3/60;
124
cout<<"\n \tKilowatt-hour consumed from IdleTemp to OxTempC="<<RTkWh3;
cout<<"\n";
kWh=kW*k;
cout<<"\n \tKilowatt-hour consumed for Process at OxTempC="<<kWh;
cout<<"\n";
RDkWh4=RDkW4*RD;
cout<<"\n \tKilowatt-hour consumed from OxTempC to IdleTemp ="<<RDkWh4;
cout<<"\n";
RDkWh5=RDkW5*RD4/60;
cout<<"\n \tKilowatt-hour consumed during Boat Unloading="<<RDkWh5;
kWhBUM = kWBUM*(RD4/60);
cout<<"\n \tkilowatt-hour for Boat Unloader motor is ="<<kWhBUM;
cout<<"\n";
cout<<"\n";
cout<<"\n \tThe Furnace has a total of 6 exhaust and scavenger fans 100 cfm each";
kWEFans = 0.18;
cout<<"\n \tkilowatt for Exhaust and Scavenger Fans is ="<<kWEFans;
kWhEFans = (kWEFans)*TPT;
cout<<"\n \tkilowatt-hour for Exhaust and Scavenger Fans is ="<<kWhEFans;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tThe Total Energy Consumption of the Furnace is as follows";
cout<<"\n";
kWT = (RTkW2 + RTkW3 + kWBLM + kW + RDkW4 + RDkW5 +kWBUM+ kWEFans);
cout<<"\n \tTotal kilowatt for Furnace Operation="<<kWT;
cout<<"\n";
kWhT = (RTkWh2 + RTkWh3 + kWhBLM + kWh+ RDkWh4 + RDkWh5 + kWhBUM + kWhEFans);
cout<<"\n \tTotal kilowatt-hour per cycle for Furnace Operation="<<kWhT;
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermining Controller Power (kW) and Energy (kWh)";
cout<<"\n";
cout<<"\n \tController is an equipment used to maintain the current and";
cout<<"\n \tand voltage supply requirements of the Furnace";
cout<<"\n";
CkW=1.732*3*0.9;
CT=24*7*4;
CkWh=(CkW)*TPT;
cout<<"\n \tThe Controller operates for 24 hours 7 days a week";
cout<<"\n \tand 4 weeks per month for process as well as idle temp operation";
cout<<"\n \tController Operating time per month in hours ="<<CT;
cout<<"\n \tKilowatt consumed by Controller ="<<CkW;
cout<<"\n \tKilowatt-hour consumed by Controller per cycle ="<<CkWh;
IdT1=750;
IdT2=700;
IdT3=650;
IdT4=600;
cout<<"\n";
//cout<<"\n \tDetermining RT1, Range 400 to IdleTemp";
RT1 = (IdleTemp - 400)/RUR;
//cout<<"\n \t RT1="<<RT1/60;
// For Idle temperature of 750 C
if (IdleTemp == IdT1)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8+10+11.2+12)/6;
125
}
// For Idle temperature of 700 C
if (IdleTemp == IdT2)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8+10+11.2)/5;
}
// For Idle temperature of 650 C
if (IdleTemp == IdT3)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8+10)/4;
}
// For Idle temperature of 600 C
if (IdleTemp == IdT4)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8)/3;
}
RTkW1=RTkW1max*RUR/22;
//cout<<"\n \tKilowatt consumed from 400 to IdleTemp="<<RTkW1;
//cout<<"\n \t";
//cout<<"\n \t";
//cout<<"\n \tDetermining Ramping Down Time from 750 to 400 in hours";
//cout<<"\n \t";
//RD5 = (750 - 400)/5;
//cout<<"\n \tRD5="<<RD5/60;
//cout<<"\n";
//RDkW6 = 1.732*0.9*(4+7.2+8.8+11.2+12)/5;
//cout<<"\n \tKilowatt consumed from 750 to 400 ="<<RDkW6;
cout<<"\n";
cout<<"\n \tBase Level operation";
cout<<"\n";
cout<<"\n \tThe Furnace is kept ON at idle temperature continuously for 24 hours";
cout<<"\n \t7 days a week and 4 weeks per month";
BLOkW= ((1.732*4*0.9)*RUR/22) + (RTkW1);
BLOT=24*7*4;
BLOkWh=(BLOkW)*TPT;
cout<<"\n \tBase Level operation time per month in hours="<<BLOT;
cout<<"\n \tKilowatt consumed for Base Level operation ="<<BLOkW;
cout<<"\n \tKilowatt-hour consumed for Base Level operation per cycle ="<<BLOkWh;
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermining Support equipment Power(kW)and Energy(kWh)";
cout<<"\n \trequirement";
cout<<"\n";
cout<<"\n \tSupport equipments are \n";
cout<<" \n \t1 Wafer Handler \n";
cout<<" \n \t2 Wafer Cleaner \n";
cout<<" \n \t3 Wafer Scrubber \n";
cout<<"\n";
cout<<"\n";
cout<<"\t1 The Wafer Handler is a motor operated equipment used to";
cout<<"\n \tload and unload the cassette of wafers \n";
v1=0.785*(D1*0.0254)*(D1*0.0254)*0.0006;
n=50;
Vol1=n*v1;
Mass1=2330*Vol1;
126
L2=(n*0.0006)+(0.001*(n-1));
Vol2=L2*0.2*0.01;
Mass2=2330*Vol2;
M=Mass1 + Mass2;
W=M*9.81;
T1=W*0.6096;
HP=0.746*2*3.14*1800*T1/60/1000;
cout<<"\n \tMotor Horse Power required is="<<HP;
kW1=HP*0.8/0.746/0.87;
cout<<"\n";
cout<<"\n \tWafer Handler equipment rating in kW="<<kW1;
cout<<"\n";
cout<<"\n \tTime for which Wafer Handler equipment operates";
cout<<"\n \tis same as that for sum of boat loading and unloading time";
cout<<"\n \tkilowatt for Wafer Handler equipment ="<<kW1;
kWh1=kW1*4*(RT2 + RD4)/60;
cout<<"\n \tkilowatt-hour for Wafer Handler equipment ="<<kWh1;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\t2 Wafer Cleaner is an equipment used to remove the particulates";
cout<<"\n \tfrom the wafer surface";
cout<<"\n";
cout<<"\n \tEnter Voltage and Amperage for Wafer Cleaner \n";
cout<<"\n \t";
cin>>V2>>A2;
kW2=(1.732*V2*A2*0.9*0.9)/1000.0/0.8;
cout<<"\n \tWafer Cleaner Equipment rating in kW="<<kW2;
cout<<"\n";
cout<<"\n \tPlease specify the maximum number of wafers per hour";
cout<<"\n \tthat can be processed using the Wafer cleaner \n \t";
cout<<"\n \t";
cin>>WCTP;
Ctime = thp/WCTP;
CUF = Ctime/C/TPT;
cout<<"\n \t";
cout<<"Wafer Cleaner crrent utilization factor CUF = "<<CUF;
cout<<"\n";
cout<<"\n \tkilowatt for Wafer Cleaner Equipment="<<kW2;
kWh2=kW2*(TPT*CUF);
cout<<"\n \tkilowatt-hour per cycle for Wafer Cleaner Equipment="<<kWh2;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\t3 Wafer Scrubber is an equipment used to remove the particulates \n";
cout<<"\n \twhich cannot be removed by wafer cleaner";
cout<<"\n";
cout<<"\n \tEnter Voltage and Amperage for Wafer Scrubber \n";
cout<<"\n \t";
cin>>V3>>A3;
kW3=(1.732*V3*A3*0.9*0.9)/1000.0/0.8;
cout<<"\n";
cout<<" \tWafer Scrubber Equipment rating in kW="<<kW3;
cout<<"\n";
cout<<"\n";
cout<<"\n \tkilowatt for Wafer Scrubber Equipment="<<kW3;
127
kWh3=kW3*(TPT*0.6);
cout<<"\n \tkilowatt-hour for Wafer Scrubber Equipment="<<kWh3;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tTotal Power (kW) consumed by Support Equipment="<<(kW1 + kW2 + kW3);
cout<<"\n \tTotal Energy (kWh) per cycle consumed by Support Equipment="<<(kWh1 + kWh2 + kWh3);
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tCalculating Clean Room Energy";
cout<<"\n";
cout<<"\n \tEnter the cleanroom 1 Class desired \n";
cout<<"\n \t";
cin>>Cl;
cout<<"\n";
cout<<"\n \tEnter the Cleanroom 1 volume in cubic feet \n";
cout<<"\n \t";
cin>>Volume1;
c1 = 10000;
c2 = 1000;
c3 = 100;
c4 = 10;
c5 = 1;
if (Cl == c1)
{
FFU1 = (1.25)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c2)
{
FFU1 = (3.25)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c3)
{
FFU1 = (6)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c4)
{
FFU1 = (7)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c5)
{
FFU1 = (7.5)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
cout<<"\n";
cout<<"\n";
cout<<"\n \tEnter the cleanroom 2 Class desired \n";
cout<<"\n \t";
cin>>C2;
cout<<"\n";
cout<<"\n \tEnter the Cleanroom 2 volume in cubic feet \n";
128
cout<<"\n \t";
cin>>Volume2;
c6 = 10000;
c7 = 1000;
c8 = 100;
c9 = 10;
c10 = 1;
if (C2 == c6)
{
FFU2 = (1.25)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c7)
{
FFU2 = (3.25)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c8)
{
FFU2 = (6)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c9)
{
FFU2 = (7)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c10)
{
FFU2 = (7.5)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
FFU =(FFU1+FFU2);
cout<<"\n \tTotal Fan Filter Units required for cleanroom="<<FFU;
cout<<"\n";
kW4=(FFU*0.3)*1.0*0.8/0.746/0.87;
cout<<"\n \tkilowatt consumption by FFU's ="<<kW4;
FE = kW4;
kWh4 = kW4*TPT;
cout<<"\n \tkilowatt-hour per cycle required for FFU's ="<<kWh4;
cout<<"\n";
cout<<"\n";
cout<<"\n \tPlease select the nearest total square feet dimension for the ";
cout<<"\n \tclean room ceiling area from the following choices ";
cout<<"\n \t200 sq. ft ";
cout<<"\n \t400 sq. ft ";
cout<<"\n \t600 sq. ft ";
cout<<"\n \t800 sq. ft ";
cout<<"\n \t1,000 sq. ft ";
cout<<"\n \t";
cout<<"\n \t";
cin>>Carea;
Carea1 = 200;
Carea2 = 400;
Carea3 = 600;
Carea4 = 800;
129
Carea5 = 1000;
if (Carea == Carea1)
{
AHUnits = 1;
AHFan = 1;
MUFan = 1;
}
if (Carea == Carea2)
{
AHUnits = 2;
AHFan = 2;
MUFan = 2;
}
if (Carea == Carea3)
{
AHUnits = 3;
AHFan = 3;
MUFan = 3;
}
if (Carea == Carea4)
{
AHUnits = 4;
AHFan = 4;
MUFan = 4;
}
if (Carea == Carea5)
{
AHUnits = 5;
AHFan = 5;
MUFan = 5;
}
AHkW = AHUnits*2.5*12000*0.8*293/2/1000000;
AHFkW = AHFan*10*2545*0.8*293/1000000;
MUFkW = MUFan*5*2545*0.8*293/1000000;
AHkWh = AHkW*(24*7*4);
AHFkWh = AHFkW*(24*7*4);
MUFkWh = MUFkW*(24*7*4);
cout<<"\n \tEnergy required by Air handler unit in kW ="<<AHkW;
cout<<"\n \tEnergy required by Air handler Fan unit in kW ="<<AHFkW;
cout<<"\n \tEnergy required by Make Up fan system in kW ="<<MUFkW;
cout<<"\n";
cout<<"\n";
cout<<"\n \tEnergy required by Air handler unit in kWh ="<<AHkWh;
cout<<"\n \tEnergy required by Air handler Fan unit in kWh ="<<AHFkWh;
cout<<"\n \tEnergy required by Make Up fan system in kWh ="<<MUFkWh;
cout<<"\n";
cout<<"\n \t Clean room cooling load due to equipment and operators";
cout<<"\n";
cout<<"\n \t The heat released by equipment and Personnels";
cout<<"\n \t is the cooling load for the Clean room";
cout<<"\n \t1 The euipments are insulated and considering the";
cout<<"\n \t insulation efficiency as 95% which means 5% of the equipment energy";
cout<<"\n \t is released in the clean room";
cout<<"\n \t2 The Operators act as heat emitting bodies";
cout<<"\n \t and the heat given out per operator is 800 Btu/hr";
130
HEkW = 0.05*(kWT + kW1 + kW2 + kW3 + BLOkW + CkW);
cout<<"\n \t \tHeat released from Equipment in kW is HEkW ="<<HEkW;
HEkWh = 0.05*(kWhT + kWh1 + kWh2 + kWh3 + BLOkWh + CkWh) ;
cout<<"\n \t \tHeat released from Equipments in kWh per cycle is HEkWh ="<<HEkWh;
HOkW = (800*2*293/1000000);
cout<<"\n \t \tHeat from Operators in kW is HOkW ="<<HOkW;
HOkWh = HOkW*TPT;
cout<<"\n \t \tHeat from Operators in kWh per cycle is HOkWh ="<<HOkWh;
cout<<"\n \tAlso, each Fan Filter Unit (FFU) releases heat equal to 1,000 Btu/hr";
HFFUkW = (1000*FFU*293/1000000);
cout<<"\n \t \tHeat from FFU's in kW is HFFUkW ="<<HFFUkW;
HFFUkWh = (HFFUkW*TPT);
cout<<"\n \t \tHeat from FFU's in kWh per cycle is HFFUkWh ="<<HFFUkWh;
cout<<"\n";
cout<<"\n Results";
cout<<"\n";
R1 = kWT;
cout<<"\n \tHence, Total Furnace kW consumption ="<<R1;
cout<<"\n";
R2 = (kW1 + kW2 + kW3);
cout<<"\n \t
Total Support Equipment kW consumption ="<<R2;
cout<<"\n";
R3 = (HEkW + HOkW);
cout<<"\n \t
Total Clean room Cooling load due to equipment and operators";
cout<<"\n \t
in kW ="<<R3;
cout<<"\n";
R4 = (BLOkW + CkW);
cout<<"\n \t
Total Controller and Base Level kW consumption ="<<R4;
cout<<"\n";
R5 = R1 + R2 + R3 + R4;
cout<<"\n \t
Total process kW consumption ="<<R5;
R6 = R5/thp;
cout<<"\n \t
Total kW consumption per wafer ="<<R6;
cout<<"\n";
cout<<"\n \tAdditional kW consumption by the Clean room";
cout<<"\n";
R7 = (AHkW + AHFkW + MUFkW);
cout<<"\n \t
Air handling unit, Air handler fan and Makeup fan ";
cout<<"\n \t
kW consumption="<<R7;
cout<<"\n";
R8 = kW4;
cout<<"\n \t
Total Fan filter unit kW consumption ="<<R8;
cout<<"\n";
R9 = HFFUkW;
cout<<"\n \t
Heat released from Fan filter units in kW ="<<R9;
cout<<"\n";
R10 = R7 + R8 + R9;
cout<<"\n \tAdditional kW consumption by the Clean room ="<<R10;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n";
R11 = C*kWhT;
cout<<"\n \tHence, Furnace kWh consumption per month ="<<R11;
cout<<"\n";
R12 = C*(kWh1 + kWh2 + kWh3);
131
cout<<"\n \t
Total Support Equipment kWh consumption per month ="<<R12;
cout<<"\n";
R13 = C*(BLOkWh + CkWh);
cout<<"\n \t
Controller and Base Level kWh consumption per month ="<<R13;
cout<<"\n";
R14 = C*(HEkWh + HOkWh);
cout<<"\n \t
Total Clean room Cooling load due to equipment and operators";
cout<<"\n \t
in kWh per month ="<<R14;
cout<<"\n";
R15 = R11 + R12 + R13 + R14;
cout<<"\n \t
Total process kWh consumption per month ="<<R15;
R16 = R15/thp;
cout<<"\n \t
Total kWh consumption per wafer ="<<R16;
cout<<"\n";
cout<<"\n \tAdditional kWh consumption by the Clean room";
cout<<"\n";
R17 = (AHkWh + AHFkWh + MUFkWh);
cout<<"\n \t
Air handling unit, Air handler fan and Makeup fan ";
cout<<"\n \t
kWh consumption per month ="<<R17;
cout<<"\n";
R18 = C*kWh4;
cout<<"\n \t
Total Fan filter unit kWh consumption per month ="<<R18;
cout<<"\n";
R19 = C*HFFUkWh;
cout<<"\n \t
Heat released from Fan filter units per month ="<<R19;
cout<<"\n";
R20 = R17 + R18 + R19;
cout<<"\n \tAdditional kWh consumption by the Clean room ="<<R20;
cout<<"\n";
getch();
getch();
getch();
getch();
FILE *fp;
fp=fopen("Oxidation.xls","a");
{
//printf("Throughput %f\n", thp);
fprintf(fp,"Throughput\t%f\n",thp);
//printf("Oxidation cycles per month %f\n", C);
fprintf(fp,"Oxidation cycles per month \t%f\n",C);
//printf("Wafer Diameter %f\n", D1);
fprintf(fp,"Wafer Diameter \t%f\n",D1);
//printf("Oxide Thickness %f\n", tox);
fprintf(fp,"Oxide Thickness \t%f\n",tox);
//printf("type %f\n", type);
fprintf(fp,"type \t%c\n",type);
//printf("orientation %f\n", orientation);
fprintf(fp,"orientation \t%c\n",orientation);
//printf("Oxidation Temperature %f\n", OxTempC);
fprintf(fp,"Oxidation Temperature \t%f\n",OxTempC);
//printf("Furnace Idle Temp %f\n", IdleTemp);
fprintf(fp,"Furnace Idle Temp \t%f\n",IdleTemp);
fprintf(fp,"G1 \t%f\n",G1);
fprintf(fp,"G2 \t%f\n",G2);
fprintf(fp,"k1 \t%f\n",k1);
fprintf(fp,"k2 \t%f\n",k2);
132
fprintf(fp,"k \t%f\n",k);
//printf("Ramp Up Rate %f\n", RUR);
fprintf(fp,"Ramp Up Rate \t%f\n",RUR);
//printf("Total Ramp Up Time %f\n", RT);
fprintf(fp,"Total Ramp Up Time \t%f\n",RT);
//printf("Oxidation Process Time %f\n", k);
fprintf(fp,"Oxidation Process Time \t%f\n",k);
//printf("Total Ramp Down Time %f\n", TRD);
fprintf(fp,"Total Ramp Down Time \t%f\n",TRD);
//printf("Total Process Time %f\n", TPT);
fprintf(fp,"Total Process Time \t%f\n",TPT);
//printf("Total Furnace kW consumption %f\n", R1);
fprintf(fp,"Total Furnace kW consumption \t%f\n",R1);
//printf("Total Support Equipment kW consumption %f\n", R2);
fprintf(fp,"Total Support Equipment kW consumption \t%f\n",R2);
//printf("Total Clean room Cooling load due to equipment and operators in kW %f\n", R3);
fprintf(fp,"Total Clean room Cooling load due to equipment and operators in kW \t%f\n",R3);
//printf("Total Controller and Base Level kW consumption %f\n", R4);
fprintf(fp,"Total Controller and Base Level kW consumption \t%f\n",R4);
//printf("Total process kW consumption %f\n", R5);
fprintf(fp,"Total process kW consumption \t%f\n",R5);
//printf("Total kW consumption per wafer %f\n", R6);
fprintf(fp,"Total kW consumption per wafer \t%f\n",R6);
//printf("Additional kW consumption - by the Clean room %f\n" );
fprintf(fp,"Additional kW consumption - by the Clean room \t%f\n" );
//printf("Air handling unit, Air handler fan and Makeup fan kW consumption %f\n", R7);
fprintf(fp,"Air handling unit, Air handler fan and Makeup fan kW consumption \t%f\n",R7);
//printf("Total Fan filter unit kW consumption %f\n", R8);
fprintf(fp,"Total Fan filter unit kW consumption \t%f\n",R8);
//printf("Heat released from Fan filter units in kW %f\n", R9);
fprintf(fp,"Heat released from Fan filter units in kW \t%f\n",R9);
//printf("Additional kW consumption by the Clean room %f\n", R10);
fprintf(fp,"Additional kW consumption by the Clean room \t%f\n",R10);
//printf("Hence, Total Furnace kWh consumption per month %f\n", R11);
fprintf(fp,"Hence, Total Furnace kWh consumption per month \t%f\n",R11);
//printf("Total Support Equipment kWh consumption per month %f\n", R12);
fprintf(fp,"Total Support Equipment kWh consumption per month \t%f\n",R12);
//printf("Total Controller and Base Level kWh consumption per month %f\n", R13);
fprintf(fp,"Total Controller and Base Level kWh consumption per month \t%f\n",R13);
//printf("Total Clean room Cooling load due to equipment and operators in kWh per month
%f\n", R14);
fprintf(fp,"Total Clean room Cooling load due to equipment and operators in kWh per month
\t%f\n",R14);
//printf("Total process kWh consumption per month %f\n", R15);
fprintf(fp,"Total process kWh consumption per month \t%f\n",R15);
//printf("Total kWh consumption per wafer %f\n", R16);
fprintf(fp,"Total kWh consumption per wafer \t%f\n",R16);
//printf("Additional kWh consumption - by the Clean room %f\n" );
fprintf(fp,"Additional kWh consumption - by the Clean room \t%f\n" );
//printf("Air handling unit, Air handler fan and Makeup fan kWh consumption per month
%f\n", R17);
fprintf(fp,"Air handling unit, Air handler fan and Makeup fan kWh consumption per month
\t%f\n",R17);
//printf("Total Fan filter unit kWh consumption per month %f\n", R18);
fprintf(fp,"Total Fan filter unit kWh consumption per month \t%f\n",R18);
//printf("Heat released from Fan filter units per month %f\n", R19);
133
fprintf(fp,"Heat released from Fan filter units per month \t%f\n",R19);
//printf("Additional kWh consumption by the Clean room %f\n", R20);
fprintf(fp,"Additional kWh consumption by the Clean room \t%f\n", R20);
}
fclose(fp);
cout<<"\n";
cout<<"\n Program ends";
cout<<"Press any key to exit";
cin>>exit;
case 2:
cout<<"\n";
cout<<"\t \t \t \tOperation is Doping \n \n";
cout<<"\t Doping can be defined as the process of introducing dopant atoms \n \t into the wafer surface so as to
form junctions in the wafer.\n \t Junctions can be of PN, NP, PNP, or NPN type\n";
cout<<"\n \t \t Doping is carried out in two steps, viz";
cout<<"\n \t \t 1. Pre deposition";
cout<<"\n \t \t 2. Drive in";
cout<<"\n";
cout<<"\n \t \t Pre deposition is the process of introducing dopant atoms";
cout<<"\n \t \t into the wafer surface";
cout<<"\n";
cout<<"\n \t \t Drive in is the process of driving in the dopant atoms";
cout<<"\n \t \t already introduced in the wafer at a higher temperature as";
cout<<"\n \t \t compared to the Pre deposition process";
cout<<"\n";
cout<<"\n \t \t Pre deposition process is completed in a very small period \n \t \t of time as compared to the
drive in process \n \t \t It does not have much degrees of freedom with respect to energy";
cout<<"\n \n \t \t Types of dopants used:";
cout<<"\n \t \t \t1. p type";
cout<<"\n \t \t
2. n type\n";
cout<<"\n \t \t \t p type : Boron";
cout<<"\n \t \t \t n type : Phosphorous";
cout<<"\n";
cout<<"\n";
cout<<"\n \tEnter type of dopant to be used";
cout<<"\n \tn type - n or p type - p";
cout<<"\n";
cout<<"\n \t";
cin>>dopant;
if (dopant == 110)
{
do2 = 4.7;
Ea = 3.68;
Cx2 = 3.16e20;
}
if (dopant == 112)
{
do2 = 1;
Ea = 3.5;
Cx2 = 6.76e19;
}
cout<<"\n \tDiffusion co - efficient, do2 ="<<do2;
cout<<"\n \tActivation Energy, Ea ="<<Ea;
cout<<"\n \tSurface concentration, Cx2 ="<<Cx2;
cout<<"\n";
134
cout<<"\n \tFor Pre deposition process";
cout<<"\n \tPlease enter the Pre deposition time in hours \n \t";
cout<<"\n \t";
cin>>deptime;
cout<<"\n";
cout<<"\n \tPlease enter the Pre deposition process temperature in degree celsius \n \t";
cout<<"\n \t";
cin>>deptemp;
deptempk = (deptemp + 273);
//cout<<"\n \tdeptempk ="<<deptempk;
dexp1 = exp(-Ea/0.00008625/deptempk);
//cout<<"\n \tdexp1 ="<<dexp1;
d1=dexp1*100000000*do2;
//cout<<"\n \td1 ="<<d1;
cout<<"\n";
cout<<"\n \tFor Drive in process";
cout<<"\n \tPlease enter the drive in time in hours \n \t";
cout<<"\n \t";
cin>>drintime;
cout<<"\n";
cout<<"\n \tPlease enter the drive in process temperature in degree celsius \n \t";
cout<<"\n \t";
cin>>drintemp;
drintempk = (drintemp + 273);
//cout<<"\n \tdrintempk ="<<drintempk;
dexp2 = exp(-Ea/0.00008625/drintempk);
//cout<<"\n \tdexp2 ="<<dexp2;
d2=dexp2*100000000*do2;
//cout<<"\n \td2 ="<<d2;
cout<<"\n";
cout<<"\n \tSubstrate concentration for junctions is usually 1e16 atoms/cc \t";
cout<<"\n \tUser may select the desired value in atoms/cc";
cout<<"\n \tPlease enter the substrate concentration required in atoms/cc \n \t";
cout<<"\n \t";
cin>>Csub;
Qo = 2*Cx2*(sqrt(d1*(deptime*60*60)))/(sqrt(3.1412));
//cout<<"\n \tQo ="<<Qo;
Q2= Qo/(sqrt(3.1412*d2*drintime*60*60));
cout<<"\n \tNumber of Dopant atoms introduced in predeposition (Q2) ="<<Q2;
X=log(Csub/Q2);
X1=X*4*d2*(drintime*60*60);
X2=-(X1);
Xj=sqrt(X2);
cout<<"\n \tThe Junction depth achieved after Drive in (Xj) ="<<Xj;
cout<<"\n";
cout<<"\n";
cout<<"\n \tEnter Wafer diameter in inches \n \t";
cout<<"\n \t";
cin>>D1;
cout<<"\n";
cout<<"\n \tEnter Idle Temperature of Furnace/Diffuser/Oxidizer \n \t";
cout<<"\n \t";
cin>>IdleTemp;
cout<<"\n";
cout<<"\n";
cout<<"\n \tSpecify the Ramp Up Rate per minute desired in degree Celsius \n \t";
135
cout<<"\n \t";
cin>>RUR;
cout<<"\n";
cout<<"\n \tDetermining Ramp Up Time in hours";
cout<<"\n";
cout<<"\n \t \tDetermining RT2, Wafer Boat Loading time";
cout<<"\n \t \tLoading Speed is 15 inch per minute";
RT2 = (60)/15;
cout<<"\n \t \tRT2="<<RT2/60;
cout<<"\n";
cout<<"\n \t\tDetermining RT3, Range IdleTemp to drintemp";
RT3 = (drintemp - IdleTemp)/RUR;
cout<<"\n \t \tRT3="<<RT3/60;
RT=(RT2+RT3)/60;
cout<<"\n";
cout<<"\n \tTotal Ramp Up Time in hours RT="<<RT;
cout<<"\n";
cout<<"\n \tDoping Process Time in hours ="<<drintime;
cout<<"\n";
cout<<"\n \tDetermining Ramping Down Time from drintemp to IdleTemp in hours";
x1=1300;
x2=1100;
x3=900;
x4=750;
if (drintemp <= x1 && drintemp > x2)
{
RD1 = (drintemp - 1100)/13;
RD2 = (1100 - 900)/8;
RD3 = (900 - IdleTemp)/5;
cout<<"\n";
cout<<"\t \tRD1="<<RD1/60;
cout<<"\n";
cout<<"\t \tRD2="<<RD2/60;
cout<<"\n";
cout<<"\t \tRD3="<<RD3/60;
cout<<"\n";
RD=(RD1+RD2+RD3)/60;
cout<<"\tRamp Down Time in hours RD="<<RD;
cout<<"\n";
}
if (drintemp <= x2 && drintemp > x3)
{
RD1 = (drintemp - 900)/8;
RD2 = (900 - IdleTemp)/5;
cout<<"\n";
cout<<"\t \tRD1="<<RD1/60;
cout<<"\n";
cout<<"\t \tRD2="<<RD2/60;
cout<<"\n";
RD=(RD1+RD2)/60;
cout<<"\tRamp Down Time in hours RD="<<RD;
cout<<"\n";
}
if (drintemp <= x3 && drintemp > x4)
{
136
RD1 = (drintemp - IdleTemp)/5;
cout<<"\n";
cout<<"\t \tRD1="<<RD1/60;
cout<<"\n";
RD=(RD1)/60;
cout<<"\tRamp Down Time in hours RD="<<RD;
cout<<"\n";
}
cout<<"\n \tDetermining RD4, Wafer Boat Unloading time";
cout<<"\n \tUnloading Speed is 15 inch per minute";
RD4 = (60)/15;
cout<<"\n \tRD4="<<RD4/60;
cout<<"\n";
cout<<"\n";
cout<<"\n";
TRD=RD+(RD4)/60;
cout<<"\tTotal Ramp Down Time in hours TRD="<<TRD;
cout<<"\n";
cout<<"\n";
TPT= (RT+drintime+TRD);
cout<<"\n \tTotal Process Time in hours ="<<TPT;
cout<<"\n";
cout<<"\n";
cout<<"\n \tFor a 24 x 7 operation, the throughput per month";
cout<<"\n \tis estimated as follows";
C = (24*7*4)/TPT;
cout<<"\n";
cout<<"\n \tDoping cycles per month";
cout<<"\n \t C = "<<C;
thp = C*200;
cout<<"\n \t";
cout<<"\n \tThroughput - Wafers processed per month = "<<thp;
cout<<"\n \t";
cout<<"\n";
IdT1=600;
IdT2=650;
IdT3=700;
IdT4=750;
// For Idle temperature of 600 C
if (IdleTemp == IdT1)
{
RTkW2max = 1.732*0.9*8.8;
}
// For Idle temperature of 650 C
if (IdleTemp == IdT2)
{
RTkW2max = 1.732*0.9*10;
}
// For Idle temperature of 700 C
if (IdleTemp == IdT3)
{
RTkW2max = 1.732*0.9*11.2;
}
// For Idle temperature of 750 C
if (IdleTemp == IdT4)
137
{
RTkW2max = 1.732*0.9*12;
}
cout<<"\n \tBoat Loading is done at IdleTemp. Hence, the Kilowatt";
cout<<"\n \tconsumed during Boat Loading";
RTkW2 = RTkW2max*RUR/22;
cout<<"\n \t="<<RTkW2;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tSmall motors of 0.5 hp are used for Boat Loading operation.";
cout<<"\n \tFor each of the 4 boats, a sepaerate motor is used";
kWBLM = (4*0.5*0.746);
cout<<"\n \tkilowatt for Boat Loader motor is ="<<kWBLM;
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermine Kilowatt consumed from IdleTemp to OxTempC";
IdT1=600;
IdT2=650;
IdT3=700;
IdT4=750;
y1=900;
y2=950;
y3=1000;
y4=1050;
y5=1100;
y6=1150;
y7=1200;
y8=1250;
y9=1300;
// For Idle temperature of 600 C
if (drintemp == y1 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16)/7;
}
if (drintemp == y2 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18)/8;
}
if (drintemp == y3 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6)/9;
}
if (drintemp == y4 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22)/10;
}
if (drintemp == y5 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4)/11;
}
if (drintemp == y6 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26)/12;
}
138
if (drintemp == y7 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/13;
}
if (drintemp == y8 && IdleTemp == IdT1)
{
RTkW3max = 1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/14;
}
if (drintemp == y9 && IdleTemp == IdT1)
{
RTkW3max =
1.732*0.9*(8.8+10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/15;
}
// For Idle temperature of 650 C
if (drintemp == y1 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16)/6;
}
if (drintemp == y2 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18)/7;
}
if (drintemp == y3 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6)/8;
}
if (drintemp == y4 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22)/9;
}
if (drintemp == y5 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4)/10;
}
if (drintemp == y6 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26)/11;
}
if (drintemp == y7 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/12;
}
if (drintemp == y8 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/13;
}
if (drintemp == y9 && IdleTemp == IdT2)
{
RTkW3max = 1.732*0.9*(10+11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/14;
}
// For Idle temperature of 700 C
if (drintemp == y1 && IdleTemp == IdT3)
{
139
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16)/5;
}
if (drintemp == y2 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18)/6;
}
if (drintemp == y3 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6)/7;
}
if (drintemp == y4 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22)/8;
}
if (drintemp == y5 && IdleTemp == IdT3)
{
RTkW3max = (1.732*0.9)*(11.2+12+12.8+14.4+16+18+19.6+22+24.4)/9;
}
if (drintemp == y6 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26)/10;
}
if (drintemp == y7 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/11;
}
if (drintemp == y8 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/12;
}
if (drintemp == y9 && IdleTemp == IdT3)
{
RTkW3max = 1.732*0.9*(11.2+12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/13;
}
// For Idle temperature of 750 C
if (drintemp == y1 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16)/4;
}
if (drintemp == y2 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18)/5;
}
if (drintemp == y3 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6)/6;
}
if (drintemp == y4 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22)/7;
}
if (drintemp == y5 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4)/8;
}
if (drintemp == y6 && IdleTemp == IdT4)
140
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26)/9;
}
if (drintemp == y7 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26+28.8)/10;
}
if (drintemp == y8 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8)/11;
}
if (drintemp == y9 && IdleTemp == IdT4)
{
RTkW3max = 1.732*0.9*(12+12.8+14.4+16+18+19.6+22+24.4+26+28.8+32.8+35.2)/12;
}
RTkW3 = (RTkW3max*RUR)/22;
cout<<"\n \tKilowatt consumed from IdleTemp to drintemp="<<RTkW3;
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermine Kilowatt consumed for Process at drintemp";
y1=900;
y2=950;
y3=1000;
y4=1050;
y5=1100;
y6=1150;
y7=1200;
y8=1250;
y9=1300;
if (drintemp == y1)
{
kWmax = 1.732*0.9*(16);
}
if (drintemp == y2)
{
kWmax = 1.732*0.9*(18);
}
if (drintemp == y3)
{
kWmax = 1.732*0.9*(19.6);
}
if (drintemp == y4)
{
kWmax = 1.732*0.9*(22);
}
if (drintemp == y5)
{
kWmax = (1.732*0.9)*(24.4);
}
if (drintemp == y6)
{
kWmax = 1.732*0.9*(26);
}
if (drintemp == y7)
{
kWmax = 1.732*0.9*(28.8);
141
}
if (drintemp == y8)
{
kWmax = 1.732*0.9*(32.8);
}
if (drintemp == y9)
{
kWmax = 1.732*0.9*(35.2);
}
cout<<"\n";
kW=kWmax*RUR/22;
cout<<"\n \tKilowatt consumed for Process at drintemp ="<<kW;
cout<<"\n";
cout<<"\n";
// kilowatt consumed during ramp down will be approximately equal to that consumed at idle temp
cout<<"\n \tKilowatt consumed from drintemp to IdleTemp";
RDkW4=RTkW2;
cout<<"\n \tKilowatt consumed from drintemp to IdleTemp ="<<RDkW4;
IdT1=600;
IdT2=650;
IdT3=700;
IdT4=750;
// For Idle temperature of 600 C
if (IdleTemp == IdT1)
{
RDkW5max = 1.732*0.9*8.8;
}
// For Idle temperature of 650 C
if (IdleTemp == IdT2)
{
RDkW5max = 1.732*0.9*10;
}
// For Idle temperature of 700 C
if (IdleTemp == IdT3)
{
RDkW5max = 1.732*0.9*11.2;
}
// For Idle temperature of 750 C
if (IdleTemp == IdT4)
{
RDkW5max = 1.732*0.9*12;
}
cout<<"\n";
cout<<"\n \tBoat Unloading is done at IdleTemp. Hence, the";
cout<<"\n \tKilowatt consumed during Boat Unloading";
RDkW5= RDkW5max*RUR/22;
cout<<"\n \tRDkW5="<<RDkW5;
cout<<"\n";
cout<<"\n \tSmall motors of 0.5 hp are used for Boat Unloading operation.";
cout<<"\n \tFor each of the 4 boats, a sepaerate motor is used";
kWBUM = (4*0.5*0.746);
cout<<"\n \tkilowatt for Boat Unloader motor is ="<<kWBUM;
cout<<"\n";
cout<<"\n";
cout<<"\n \tNow determining the Process Energy as a function of time i.e. kWh";
cout<<"\n";
142
RTkWh2=RTkW2*RT2/60;
cout<<"\n \tKilowatt-hour consumed per cycle during Wafer Boat Loading ="<<RTkWh2;
kWhBLM = kWBLM*(RD4/60);
cout<<"\n \tkilowatt-hour per cycle for Boat Loader motor is ="<<kWhBLM;
cout<<"\n";
RTkWh3=RTkW3*RT3/60;
cout<<"\n \tKilowatt-hour consumed per cycle from idle temp to drintemp ="<<RTkWh3;
cout<<"\n";
kWh=kW*drintime;
cout<<"\n \tKilowatt-hour consumed per cycle for Process at drintemp ="<<kWh;
cout<<"\n";
RDkWh4=RDkW4*RD;
cout<<"\n \tKilowatt-hour consumed per cycle from drintemp to idle temp ="<<RDkWh4;
cout<<"\n";
RDkWh5=RDkW5*RD4/60;
cout<<"\n \tKilowatt-hour consumed per cycle during Wafer Boat Unloading ="<<RDkWh5;
kWhBUM = kWBUM*(RD4/60);
cout<<"\n \tkilowatt-hour per cycle for Boat Unloader motor is ="<<kWhBUM;
cout<<"\n";
cout<<"\n";
cout<<"\n \tThe Furnace has a total of 6 exhaust and scavenger fans 100 cfm each";
kWEFans = 0.18;
cout<<"\n \tkilowatt for Exhaust and Scavenger Fans is ="<<kWEFans;
kWhEFans = (kWEFans)*TPT;
cout<<"\n \tkilowatt-hour for Exhaust and Scavenger Fans is ="<<kWhEFans;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tThe Total Energy Consumption of the Furnace is as follows";
cout<<"\n";
kWT = (RTkW2 + RTkW3 + kWBLM + kW + RDkW4 + RDkW5 +kWBUM+ kWEFans);
cout<<"\n \tTotal kilowatt for Furnace Operation="<<kWT;
kWhT = (RTkWh2 + RTkWh3 + kWhBLM + kWh+ RDkWh4 + RDkWh5 + kWhBUM + kWhEFans);
cout<<"\n \tTotal kilowatt-hour per cycle for Furnace Operation="<<kWhT;
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermining Controller Power (kW) and Energy (kWh)";
cout<<"\n";
cout<<"\n \tController is an equipment used to maintain the current and";
cout<<"\n \tand voltage supply requirements of the Furnace";
cout<<"\n";
CkW=1.732*3*0.9;
CT=24*7*4;
CkWh=(CkW)*TPT;
cout<<"\n \tThe Controller operates for 24 hours 7 days a week";
cout<<"\n \tand 4 weeks per month for process as well as idle temp operation";
cout<<"\n \tController Operating time per month in hours ="<<CT;
cout<<"\n \tKilowatt consumed by Controller ="<<CkW;
cout<<"\n \tKilowatt-hour consumed by Controller per cycle ="<<CkWh;
cout<<"\n";
cout<<"\n";
IdT1=750;
IdT2=700;
IdT3=650;
IdT4=600;
cout<<"\n";
143
//cout<<"\n \tDetermining RT1, Range 400 to IdleTemp";
RT1 = (IdleTemp - 400)/RUR;
//cout<<"\n \t RT1="<<RT1/60;
cout<<"\n";
cout<<"\n";
// For Idle temperature of 750 C
if (IdleTemp == IdT1)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8+10+11.2+12)/6;
}
// For Idle temperature of 700 C
if (IdleTemp == IdT2)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8+10+11.2)/5;
}
// For Idle temperature of 650 C
if (IdleTemp == IdT3)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8+10)/4;
}
// For Idle temperature of 600 C
if (IdleTemp == IdT4)
{
RTkW1max = 1.732*0.9*(4+7.2+8.8)/3;
}
RTkW1=RTkW1max*RUR/22;
//cout<<"\n \tKilowatt consumed from 400 to IdleTemp="<<RTkW1;
//cout<<"\n \t";
//cout<<"\n \t";
//cout<<"\n \tDetermining Ramping Down Time from 750 to 400 in hours";
//cout<<"\n \t";
//RD5 = (750 - 400)/5;
//cout<<"\n \tRD5="<<RD5/60;
//cout<<"\n";
//RDkW6 = 1.732*0.9*(4+7.2+8.8+11.2+12)/5;
//cout<<"\n \tKilowatt consumed from 750 to 400 ="<<RDkW6;
cout<<"\n";
cout<<"\n \tBase Level operation";
cout<<"\n";
cout<<"\n \tThe Furnace is kept ON at idle temperature continuously for 24 hours";
cout<<"\n \t7 days a week and 4 weeks per month";
BLOkW= ((1.732*4*0.9)*RUR/22) + (RTkW1);
BLOT=24*7*4;
BLOkWh=(BLOkW)*TPT;
cout<<"\n \tBase Level operation time per month in hours="<<BLOT;
cout<<"\n \tKilowatt consumed for Base Level operation ="<<BLOkW;
cout<<"\n \tKilowatt-hour consumed for Base Level operation per cycle ="<<BLOkWh;
cout<<"\n";
cout<<"\n";
cout<<"\n \tDetermining Support equipment Power(kW)and Energy(kWh)";
cout<<"\n \trequirement";
cout<<"\n";
cout<<"\n \tSupport equipments are \n";
cout<<" \n \t1 Wafer Handler \n";
cout<<" \n \t2 Wafer Cleaner \n";
cout<<" \n \t3 Wafer Scrubber \n";
144
cout<<"\n";
cout<<"\n";
cout<<"\t1 The Wafer Handler is a motor operated equipment used to";
cout<<"\n \tload and unload the cassette of wafers \n";
v1=0.785*(D1*0.0254)*(D1*0.0254)*0.0006;
n=50;
Vol1=n*v1;
Mass1=2330*Vol1;
L2=(n*0.0006)+(0.001*(n-1));
Vol2=L2*0.2*0.01;
Mass2=2330*Vol2;
M=Mass1 + Mass2;
W=M*9.81;
T1=W*0.6096;
HP=0.746*2*3.14*1800*T1/60/1000;
cout<<"\n \tMotor Horse Power required is="<<HP;
kW1=HP*0.8/0.746/0.87;
cout<<"\n";
cout<<"\n \tWafer Handler equipment rating in kW="<<kW1;
cout<<"\n";
cout<<"\n \tTime for which Wafer Handler equipment operates";
cout<<"\n \tis same as that for sum of boat loading and unloading time.";
cout<<"\n";
cout<<"\n \tkilowatt for Wafer Handler equipment ="<<kW1;
kWh1=kW1*4*(RT2 + RD4)/60;
cout<<"\n \tkilowatt-hour for Wafer Handler equipment ="<<kWh1;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\t2 Wafer Cleaner is an equipment used to";
cout<<"\n \tremove the particulates from the wafer surface";
cout<<"\n";
cout<<"\n \tEnter Voltage and Amperage for Wafer Cleaner \n";
cout<<"\n \t";
cin>>V2>>A2;
kW2=(1.732*V2*A2*0.9*0.9)/1000.0/0.8;
cout<<"\n \tWafer Cleaner Equipment rating in kW="<<kW2;
cout<<"\n";
cout<<"\n \tPlease specify the maximum number of wafers per hour";
cout<<"\n \tthat can be processed using the Wafer cleaner \n \t";
cout<<"\n \t";
cin>>WCTP;
Ctime = thp/WCTP;
CUF = Ctime/C/TPT;
cout<<"\n \t";
cout<<"Wafer Cleaner crrent utilization factor CUF = "<<CUF;
cout<<"\n";
cout<<"\n \tkilowatt for Wafer Cleaner Equipment="<<kW2;
kWh2=kW2*(TPT*CUF);
cout<<"\n \tkilowatt-hour for Wafer Cleaner Equipment="<<kWh2;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\t3 Wafer Scrubber is an equipment used to remove";
cout<<"\n \tthe particulates that cannot be removed by wafer cleaner";
cout<<"\n";
145
cout<<"\n \tEnter Voltage and Amperage for Wafer Scrubber \n";
cout<<"\n \t";
cin>>V3>>A3;
kW3=(1.732*V3*A3*0.9*0.9)/1000.0/0.8;
cout<<"\n";
cout<<"\n \tkilowatt for Wafer Scrubber Equipment="<<kW3;
kWh3=kW3*(TPT*0.6);
cout<<"\n \tkilowatt-hour for Wafer Scrubber Equipment="<<kWh3;
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tkilowatt consumed by Support Equipment="<<(kW1 + kW2 + kW3);
cout<<"\n \tkilowatt - hour consumed by Support Equipment="<<(kWh1 + kWh2 + kWh3);
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tCalculating Clean Room Energy";
cout<<"\n";
cout<<"\n \tEnter the cleanroom 1 Class desired \n";
cout<<"\n \t";
cin>>Cl;
cout<<"\n";
cout<<"\n \tEnter the Cleanroom 1 volume in cubic feet \n";
cout<<"\n \t";
cin>>Volume1;
c1 = 10000;
c2 = 1000;
c3 = 100;
c4 = 10;
c5 = 1;
cout<<"\n";
if (Cl == c1)
{
FFU1 = (1.25)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c2)
{
FFU1 = (3.25)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c3)
{
FFU1 = (6)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c4)
{
FFU1 = (7)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
if (Cl == c5)
{
FFU1 = (7.5)*(Volume1)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 1="<<FFU1;
}
146
cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n \tEnter the cleanroom 2 Class desired \n";
cout<<"\n \t";
cin>>C2;
cout<<"\n";
cout<<"\n \tEnter the Cleanroom 2 volume in cubic feet \n";
cout<<"\n \t";
cin>>Volume2;
c6 = 10000;
c7 = 1000;
c8 = 100;
c9 = 10;
c10 = 1;
if (C2 == c6)
{
FFU2 = (1.25)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c7)
{
FFU2 = (3.25)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c8)
{
FFU2 = (6)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c9)
{
FFU2 = (7)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
if (C2 == c10)
{
FFU2 = (7.5)*(Volume2)/(650);
cout<<"\n \tFan Filter Units required for cleanroom 2="<<FFU2;
}
FFU =(FFU1+FFU2);
cout<<"\n \tTotal Fan Filter Units required for cleanroom="<<FFU;
cout<<"\n";
kW4=(FFU*0.3)*1.0*0.8/0.746/0.87;
cout<<"\n \tkilowatt consumption by FFU's ="<<kW4;
FE = kW4;
kWh4 = kW4*TPT;
cout<<"\n \tkilowatt-hour per cycle required for FFU's ="<<kWh4;
cout<<"\n";
cout<<"\n";
cout<<"\n \tPlease select the nearest total square feet dimension for the ";
cout<<"\n \tclean room ceiling area from the following choices ";
cout<<"\n \t200 sq. ft ";
cout<<"\n \t400 sq. ft ";
147
cout<<"\n \t600 sq. ft ";
cout<<"\n \t800 sq. ft ";
cout<<"\n \t1,000 sq. ft ";
cout<<"\n \t";
cout<<"\n \t";
cin>>Carea;
Carea1 = 200;
Carea2 = 400;
Carea3 = 600;
Carea4 = 800;
Carea5 = 1000;
if (Carea == Carea1)
{
AHUnits = 1;
AHFan = 1;
MUFan = 1;
}
if (Carea == Carea2)
{
AHUnits = 2;
AHFan = 2;
MUFan = 2;
}
if (Carea == Carea3)
{
AHUnits = 3;
AHFan = 3;
MUFan = 3;
}
if (Carea == Carea4)
{
AHUnits = 4;
AHFan = 4;
MUFan = 4;
}
if (Carea == Carea5)
{
AHUnits = 5;
AHFan = 5;
MUFan = 5;
}
AHkW = AHUnits*2.5*12000*0.8*293/2/1000000;
AHFkW = AHFan*10*2545*0.8*293/1000000;
MUFkW = MUFan*5*2545*0.8*293/1000000;
AHkWh = AHkW *(24*7*4);
AHFkWh = AHFkW*(24*7*4);
MUFkWh = MUFkW*(24*7*4);
//USE MOTOR EFF ALSO FROM MM 3+
cout<<"\n \tEnergy required by Air handler unit in kW ="<<AHkW;
cout<<"\n \tEnergy required by Air handler Fan unit in kW ="<<AHFkW;
cout<<"\n \tEnergy required by Make Up fan system in kW ="<<MUFkW;
cout<<"\n \tEnergy required by Air handler unit ="<<AHkWh;
cout<<"\n \tEnergy required by Air handler Fan unit ="<<AHFkWh;
cout<<"\n \tEnergy required by Make Up fan system ="<<MUFkWh;
cout<<"\n";
cout<<"\n \t Clean room cooling load due to equipment and operators";
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cout<<"\n";
cout<<"\n \t The heat released by equipment and Personnels";
cout<<"\n \t is the cooling load for the Clean room";
cout<<"\n \t1 The euipments are insulated and considering the";
cout<<"\n \t insulation efficiency as 95% which means 5% of the equipment energy";
cout<<"\n \t is released in the clean room";
cout<<"\n \t2 The Operators act as heat emitting bodies";
cout<<"\n \t and the heat given out per operator is 800 Btu/hr";
HEkW = 0.05*(kWT + kW1 + kW2 + kW3 + BLOkW + CkW);
cout<<"\n \t \tHeat released from Equipment in kW is HEkW ="<<HEkW;
HEkWh = 0.05*(kWhT + kWh1 + kWh2 + kWh3 + BLOkWh + CkWh) ;
cout<<"\n \t \tHeat released from Equipments in kWh per cycle is HEkWh ="<<HEkWh;
HOkW = (800*2*293/1000000);
cout<<"\n \t \tHeat from Operators in kW is HOkW ="<<HOkW;
HOkWh = HOkW*TPT;
cout<<"\n \t \tHeat from Operators in kWh per cycle is HOkWh ="<<HOkWh;
cout<<"\n \t \tAlso, each Fan Filter Unit (FFU) releases heat equal to 1,000 Btu/hr";
HFFUkW = (1000*FFU*293/1000000);
cout<<"\n \t \tHeat from FFU's in kW is HFFUkW ="<<HFFUkW;
HFFUkWh = (HFFUkW*TPT);
cout<<"\n \t \tHeat from FFU's in kWh per cycle is HFFUkWh ="<<HFFUkWh;
cout<<"\n";
cout<<"\n Results";
cout<<"\n";
R1 = kWT;
cout<<"\n \tHence, Total Furnace kW consumption ="<<R1;
cout<<"\n";
R2 = (kW1 + kW2 + kW3);
cout<<"\n \t
Total Support Equipment kW consumption ="<<R2;
cout<<"\n";
R3 = (HEkW + HOkW);
cout<<"\n \t
Total Clean room Cooling load due to equipment and operators";
cout<<"\n \t
in kW ="<<R3;
cout<<"\n";
R4 = (BLOkW + CkW);
cout<<"\n \t
Total Controller and Base Level kW consumption ="<<R4;
cout<<"\n";
R5 = R1 + R2 + R3 + R4;
cout<<"\n \t
Total process kW consumption ="<<R5;
R6 = R5/thp;
cout<<"\n \t
Total kW consumption per wafer ="<<R6;
cout<<"\n";
cout<<"\n \t
Additional kW consumption by the Clean room";
cout<<"\n";
R7 = (AHkW + AHFkW + MUFkW);
cout<<"\n \t
Air handling unit, Air handler fan and Makeup fan ";
cout<<"\n \t
kW consumption ="<<R7;
cout<<"\n";
R8 = kW4;
cout<<"\n \t
Total Fan filter unit kW consumption ="<<R8;
cout<<"\n";
R9 = HFFUkW;
cout<<"\n \t
Heat released from Fan filter units in kW ="<<R9;
cout<<"\n";
R10 = R7 + R8 + R9;
cout<<"\n \tAdditional kW consumption by the Clean room ="<<R10;
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cout<<"\n";
cout<<"\n";
cout<<"\n";
cout<<"\n";
R11 = C*kWhT;
cout<<"\n \tHence, Total Furnace kWh consumption per month ="<<R11;
cout<<"\n";
R12 = C*(kWh1 + kWh2 + kWh3);
cout<<"\n \t
Total Support Equipment kWh consumption per month ="<<R12;
cout<<"\n";
R13 = C*(BLOkWh + CkWh);
cout<<"\n \t
Total Controller and Base Level kWh consumption per month="<<R13;
cout<<"\n";
R14 = C*(HEkWh + HOkWh);
cout<<"\n \t
Total Clean room Cooling load due to equipment and operators";
cout<<"\n \t
in kWh per month ="<<R14;
cout<<"\n";
R15 = R11 + R12 + R13 + R14;
cout<<"\n \t
Total process kWh consumption per month ="<<R15;
R16 = R15/thp;
cout<<"\n \t
Total kWh consumption per wafer ="<<R16;
cout<<"\n";
cout<<"\n \t
Additional kWh consumption by the Clean room";
cout<<"\n";
R17 = (AHkWh + AHFkWh + MUFkWh);
cout<<"\n \t
Air handling unit, Air handler fan and Makeup fan kWh consumption";
cout<<"\n \t
per month ="<<R17;
cout<<"\n";
R18 = C*kWh4;
cout<<"\n \t
Total Fan filter unit kWh consumption per month ="<<R18;
cout<<"\n";
R19 = C*HFFUkWh;
cout<<"\n \t
Heat released from Fan filter units per month ="<<R19;
cout<<"\n";
R20 = R17 + R18 + R19;
cout<<"\n \tAdditional kWh consumption by the Clean room ="<<R20;
cout<<"\n";
FILE *fa;
fa=fopen("Doping.xls","a");
{
//printf("dopant %f\n", dopant);
fprintf(fa,"dopant \t%c\n",dopant);
//printf("Pre deposition time %f\n", deptime);
fprintf(fa,"Pre deposition time \t%f\n",deptime);
//printf("Pre deposition temperature %f\n", deptemp);
fprintf(fa,"Pre deposition temperature \t%f\n",deptemp);
//printf("Drive in time %f\n", drintime);
fprintf(fa,"Drive in time \t%f\n",drintime);
//printf("Drive in temperature %f\n", drintemp);
fprintf(fa,"Drive in temperature \t%f\n",drintemp);
//printf("Substrate concentration %f\n", Csub);
fprintf(fa,"Substrate concentration \t%f\n",Csub);
//printf("Junction Depth %f\n", Xj);
fprintf(fa,"Junction Depth \t%f\n",Xj);
//printf("Throughput %f\n", thp);
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fprintf(fa,"Throughput\t%f\n",thp);
//printf("Doping cycles per month %f\n", C);
fprintf(fa,"Doping cycles per month \t%f\n",C);
//printf("Wafer Diameter %f\n", D1);
fprintf(fa,"Wafer Diameter \t%f\n",D1);
//printf("Furnace Idle Temp %f\n", IdleTemp);
fprintf(fa,"Furnace Idle Temp \t%f\n",IdleTemp);
//printf("Ramp Up Rate %f\n", RUR);
fprintf(fa,"Ramp Up Rate \t%f\n",RUR);
//printf("Total Ramp Up Time %f\n", RT);
fprintf(fa,"Total Ramp Up Time \t%f\n",RT);
//printf("Doping Process Time %f\n", drintime);
fprintf(fa,"Doping Process Time \t%f\n",drintime);
//printf("Total Ramp Down Time %f\n", TRD);
fprintf(fa,"Total Ramp Down Time \t%f\n",TRD);
//printf("Total Process Time %f\n", TPT);
fprintf(fa,"Total Process Time \t%f\n",TPT);
//printf("Total Furnace kW consumption %f\n", R1);
fprintf(fa,"Total Furnace kW consumption \t%f\n",R1);
//printf("Total Support Equipment kW consumption %f\n", R2);
fprintf(fa,"Total Support Equipment kW consumption \t%f\n",R2);
//printf("Total Clean room Cooling load due to equipment and operators in kW %f\n", R3);
fprintf(fa,"Total Clean room Cooling load due to equipment and operators in kW \t%f\n",R3);
//printf("Total Controller and Base Level kW consumption %f\n", R4);
fprintf(fa,"Total Controller and Base Level kW consumption \t%f\n",R4);
//printf("Total process kW consumption %f\n", R5);
fprintf(fa,"Total process kW consumption \t%f\n",R5);
//printf("Total kW consumption per wafer %f\n", R6);
fprintf(fa,"Total kW consumption per wafer \t%f\n",R6);
//printf("Additional kW consumption - by the Clean room %f\n" );
fprintf(fa,"Additional kW consumption - by the Clean room\t%f\n" );
//printf("Air handling unit, Air handler fan and Makeup fan kW consumption %f\n", R7);
fprintf(fa,"Air handling unit, Air handler fan and Makeup fan kW consumption \t%f\n",R7);
//printf("Total Fan filter unit kW consumption %f\n", R8);
fprintf(fa,"Total Fan filter unit kW consumption \t%f\n",R8);
//printf("Heat released from Fan filter units in kW %f\n", R9);
fprintf(fa,"Heat released from Fan filter units in kW \t%f\n",R9);
//printf("Additional kW consumption by the Clean room %f\n", R10);
fprintf(fa,"Additional kW consumption by the Clean room \t%f\n",R10);
//printf("Hence, Total Furnace kWh consumption per month %f\n", R11);
fprintf(fa,"Hence, Total Furnace kWh consumption per month \t%f\n",R11);
//printf("Total Support Equipment kWh consumption per month %f\n", R12);
fprintf(fa,"Total Support Equipment kWh consumption per month \t%f\n",R12);
//printf("Total Controller and Base Level kWh consumption per month %f\n", R13);
fprintf(fa,"Total Controller and Base Level kWh consumption per month \t%f\n",R13);
//printf("Total Clean room Cooling load due to equipment and operators in kWh per month
%f\n", R14);
fprintf(fa,"Total Clean room Cooling load due to equipment and operators in kWh per month
\t%f\n",R14);
//printf("Total process kWh consumption per month %f\n", R15);
fprintf(fa,"Total process kWh consumption per month \t%f\n",R15);
//printf("Total kWh consumption per wafer %f\n", R16);
fprintf(fa,"Total kWh consumption per wafer \t%f\n",R16);
//printf("Additional kWh consumption - by the Clean room %f\n" );
fprintf(fa,"Additional kWh consumption - by the Clean room \t%f\n" );
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//printf("Air handling unit, Air handler fan and Makeup fan kWh consumption per month
%f\n", R17);
fprintf(fa,"Air handling unit, Air handler fan and Makeup fan kWh consumption per month
\t%f\n",R17);
//printf("Total Fan filter unit kWh consumption per month %f\n", R18);
fprintf(fa,"Total Fan filter unit kWh consumption per month \t%f\n",R18);
//printf("Heat released from Fan filter units per month %f\n", R19);
fprintf(fa,"Heat released from Fan filter units per month \t%f\n",R19);
//printf("Additional kWh consumption by the Clean room %f\n", R20);
fprintf(fa,"Additional kWh consumption by the Clean room \t%f\n", R20);
}
fclose(fa);
cout<<"\n";
cout<<"\n Program ends";
cout<<"Press any key to exit";
cin>>exit;
}
}
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