Quantifying the Source of Reentrant Line Variability and the Effects of Processes
Standardization on Tool Availability Variability
by
Milo Camp Peavey
Bachelor of Science in Civil and Environmental Engineering,
University of Vermont (2002)
Submitted to the Department of Civil and Environmental Engineering
and
The MIT Sloan School of Management
In Partial Fulfillment of the Requirements for the Degrees of
Master of Science in Civil Engineering
Master of Business Administration
In Conjunction with the Leaders for Manufacturing Program at the
Massachusetts Institute of Technology
June 2007.
( 2007 Massachusetts Institute of Technology.
All rights reserved.
Signature of Author
M /'
epartment of Civ* 'd
an School of Management
Environmental Engineering
May 11, 2007
Certified by
(YT
Roy E. Welsch
Professor of Statistics and Management Science
Certified by
David Simchi-Levi
Professor of Engineering Systems and Civil and Environmental Engineering
Accepted by
Debbie Berechman
Executive Director, MIT Sloan MBA Program
Accepted by
Daniele Veneziano
Chairman, Departmental Committee for Graduate Students
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
JUN 0 7 2007
LIBRARIES
BARKER
Quantifying the Source of Reentrant Line Variability and the Effects of Processes
Standardization on Tool Availability Variability
by
Milo Camp Peavey
Submitted to the Sloan School of Management and the Department of Civil and
Environmental Engineering in Partial Fulfillment of the Requirements for the Degrees of
Master of Business Administration
and
Master of Science in Civil and Environmental Engineering
Abstract
This thesis quantifies the sensitivity of tool availability variability with respect to product
throughput and examines how Intel's High Precision Maintenance initiative can be used
to minimize these effects. Tools with variable availability release spikes of material into
a route which can cause down stream areas to experience irregular queues. The reentrant
multi product loops typical to Intel's manufacturing processes can make it difficult to
identify the source of long queues. The variability analysis, developed during the
internship uses D2 facility availability and cycle time data to generate a variability
correlation, called TAC-TOOT which identifies tools within the facility contributing to
throughput time.
The High Precision Maintenance initiative is an Intel developed program which focuses
on the standardization of maintenance processes. The success of the High Precision
Maintenance initiative is closely linked to the ability of factory management to motivate
equipment technicians. The thesis examines a number of tools with highly variable
availability, the effects of the high precision initiative on variability and levers factory
management can use to motivate technicians.
Thesis Advisors:
Roy Welsch
Professor of Management, Sloan School of Management
David Simchi-Levi
Professor of Engineering Systems and Civil & Environmental Engineering,
Department of Civil and Environmental Engineering
Acknowledgements
I would like to thank Intel for providing an excellent internship opportunity. Chris Keith,
my Intel supervisor was incredibly supportive and encouraging. The equipment
technicians at D2 were amazingly open to new ideas and supportive of my participation
on the team. Together they created a stimulating and amazing internship.
I would also like to thank my fiance, Nick Werner, who was incredibly supportive. He
was always home welcoming me with open arms, giving me great words of
encouragement and of course a gourmet dinner on the table. I can't wait to give up those
monthly cross country flights and be by your side.
I would like to thank my advisors, Roy Welsch and David Simchi-Levi for both
encouraging and challenging me through the internship and thesis writing process. You
both have been instrumental in making this learning experience truly valuable.
Finally, I would like to thank the LFM program for providing one of the most amazing
experiences in my life. I will never forget these two years and hope to be able to
contribute to the program's continued success.
5
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6
Table of Contents
ABSTRACT
.................................................................
3
ACKNOWLEDGEMENTS......................................................
5
TABLE OF CONTENTS........................................................
7
TABLE OF FIGURES
9
..........................................................
CHAPTER 1: INTRODUCTION .............................................................................................................
10
10
1.1 O V ER V IEW ..........................................................................................................................................
1.2 PROBLEM STATEMENT...................................................................
1.3 APPROACH.............................................................................
1.4 OUTLIN E O F TH ESIS ............................................................................................................................
11
12
CHAPTER 2: INTEL BACKGROUND...............................................................................................
13
11
2 .1 IN TE L ..................................................................................................................................................
2.2 D 2 FACTORY
...........................................................................
13
CHAPTER 3: VARIABILITY AND SEMICONDUCTOR THROUGHPUT..................................
15
3.1 POLLACZEK - KHINTCHINE FORMULA ..............................................................................................
15
3 .2 A 80 M ETR IC.......................................................................................................................................
17
13
3.3 INTEL WHITE PAPER - IMPROVING OUTPUT CAPABILITY THROUGH MINIMIZING VARIABILITY ........... 18
3.4 ALLEN & CAPELLEN VARIABILITY MODEL ........................................................................................
19
3 .5 C ON C LU SIO N ......................................................................................................................................
20
CHAPTER 4: THE TAC-TOOT VARIABILITY ANALYSIS..............................................................
4.1 VARIABILITY CALCULATION EXPLANATION.......................................................................................
21
21
Cycle Tim e Variability ........................................................................................................................
A vailability Variability........................................................................................................................
21
23
4.2 TAC-TOOT ANALYSIS EXPLANATION............................................................................................
23
Semiconductor Fab Promodel Simulation ........................................................................................
Sp earman Correlation........................................................................................................................
SpreadsheetAnalysis .......................................................................................
4.3 CONCLUSION
23
25
26
.................................................................................................... 28
CHAPTER 5: HIGH PRECISION MAINTENANCE............................................................................
29
5.1 FORM ULA 1 P IT CREW S ......................................................................................................................
5 .2 O N LIN E TOO LS ...................................................................................................................................
29
30
Fuzion 's PM OS ...................................................................................................................................
Tool Scheduling System ......................................................................................................................
5.2 IMPLEM ENTATION M ODEL AT D 2 ...................................................................................................
30
31
31
5.3 HPM O RGANIZATIONAL STRUCTURE .................................................................................................
5 .4 CO N C LU SIO N ......................................................................................................................................
32
33
CHAPTER 6: PREVENTATIVE MAINTENANCE TEAM ............................................................
34
6.1 TEAM ORGANIZATION & ACCOUNTABILITY ....................................................................................
6 .2 FLEX IB ILIT Y .......................................................................................................................................
6.3 T RA IN IN G & EDUCATION ....................................................................................................................
6.4 PM EXECUTION M ETHODOLOGY .........................................................................................................
34
35
35
36
T o o l K its..............................................................................................................................................
Parts Clean .........................................................................................................................................
36
36
7
R un the PM .........................................................................................................................................
6.5 D ATA SY STEM S ..................................................................................................................................
6.6 C ON CLU SIO N ......................................................................................................................................
37
CHAPTER 7: OBSERVATIONS AND RECOMMENDATIONS .....................................................
42
7.1 PM T EAM EX PANSION ........................................................................................................................
42
Team Ow nership.................................................................................................................................
Management Support..........................................................................................................................
Continuous imp rovem ent ....................................................................................................................
42
43
44
7.3 FUTURE A REAS OF RESEARCH ............................................................................................................
7 .4 C ON C LU SIO N ......................................................................................................................................
44
45
38
41
REFEREN CES...........................................................................................................................................
46
APPENDIX A: CYCLE TIME DISTRIBUTION OF 28 AVERAGE DAILY PERIODS FOR 35
RANDOMLY SELECTED PROCESSES............................................................................................
47
APPENDIX B: CYCLE TIME DISTRIBUTION OF 14 AVERAGE DAILY PERIODS FOR 35
RANDOMLY SELECTED PROCESSES............................................................................................
51
APPENDIX C: AVAILABILITY DISTRIBUTION OF 28 AVERAGE DAILY PERIODS FOR 11
55
RANDOMLY SELECTED PROCESSES............................................................................................
APPENDIX D: AVAILABILITY DISTRIBUTION OF 14 AVERAGE DAILY PERIODS FOR 11
56
RANDOMLY SELECTED PROCESSES............................................................................................
8
Table of Figures
FIGURE 1 EFFECTS OF UTILIZATION AND ARRIVAL RATE VARIABILITY ON W IP ........................................
FIGURE 2 VARIABILITY EXAMPLE .................................................................................................................
FIGURE 3 RELATIONSHIP OF CYCLE TIME VARIABILITY WITH PROCESSES TIME. ..........................................
FIGURE 4 LINEAR PROCESS EXAMPLE .........................................................................................................
FIGURE 5 VARIABILITY CALCULATION EXAMPLE ......................................................................................
FIGURE 6 HPM INITIATIVE STRUCTURE .....................................................................................................
FIGURE 7 D2 WORK SCHEDULE ....................................................................................................................
FIGURE 8 PM PROGRESS TRACKING..............................................................................................................
FIGURE 9 TOOL PM DOWN TIME TRACKING ................................................................................................
16
19
22
24
27
32
34
39
40
9
Chapter 1: Introduction
1.1 Overview
The key challenge for Intel is moving product quickly to the global semiconductor
market. Intel depends on the operating efficiency and capacity of its factories to give it
the agility to respond to fluctuating market demand. Semiconductor factories face unique
layout challenges because the flow of material though the manufacturing line is reentrant.
Reentrant flow is material that is processed by the same sequence of tools repeatedly. A
small increase in tool process time is magnified in wafer throughput time since the wafer
is processed by the tool multiple times.
Intel's Factories use an Automated Material Handling System (AMHS) to move wafers
quickly through the nonlinear reentrant process flow. The AMHS can move wafers
between tools at opposite ends of the factory with minimal affects to wafer travel time.
Additionally the AMHS handles the storage of work in progress (WIP) when tools are
unavailable to process it. Automated WIP movement and storage increases factory
utilization but removes many of the visual indicators associated with large volumes of
WIP.
These visual indicators allow factory management to understand how well the line is
operating by looking at the distribution of WIP across the factory floor. When WIP
collects in one area, it is simple for factory management to see that there is an issue with
one of the tools on the line. In the case of Intel, because material is transported by the
AMHS and routed in reentrant flow it is difficult to understand where large volumes of
WIP were generated and how they impact the throughput of the factory.
10
1.2 Problem Statement
This thesis focuses on identifying tools which affect the cycle time of downstream
processes and using the High Precision Maintenance program to mitigate these effects.
Tools which affect the cycle time of downstream processes typically have high capacity
and variable availability. Such tools are frequently down but their high capacity allows
them to quickly process any built up WIP. This releases a WIP bubble into the line
which moves through the following process steps until it reaches a tool with lower
capacity than the original tool. The lower capacity tool cannot process the material as
fast as it arrives and a queue begins to accumulate. Cycles such as this one have serious
impact on wafer throughput because WIP passes through both queues multiple times in
reentrant process flow.
Intel has developed excellent online WIP monitoring systems which give management
the ability to observe WIP volumes at individual tool locations. While it is important to
understand where WIP is accumulating, it is even more important to understand what is
causing these elevated levels of WIP. This thesis develops a variability analysis, called
TAC-TOOT which identifies tools which repeatedly generate WIP bubbles affecting the
cycle time of downstream operations.
The High Precision Maintenance (HPM) initiative is a program Intel created to minimize
availability variability by standardizing tool preventative maintenance. The initiative,
similar to the Toyota Production Method uses the concept that tool preventative
maintenance should be treated the same way pit crews on Formula 1 race cars approach
pit stops. The crew should be completely prepared with tools and parts in place prior to
the equipment being taken down. Once the tool is taken down, the maintenance
processes is standardized so that its success is completely independent of the equipment
technician. This thesis evaluates the success of Intel's HPM program.
1.3 Approach
Research for this thesis begins with the evaluation of the HPM effort at the D2 Fab which
was started a few months before research commenced. During the first month of
11
research, a new experimental preventive maintenance team in Area C was formed. In
addition, equipment engineers in Area C also begin drastically reducing the frequency at
which preventive maintenance is performed. Both efforts have significant effects on the
area's availability but it is difficult to separate the contributions of each effort. The TACTOOT analysis is developed to help the preventative maintenance team understand how
the variability in Area C affected cycle times in downstream areas. The use of the TACTOOT analysis is further expanded to help factory management identify other areas
which are affecting the downstream cycle times of other processes.
1.4 Outline of Thesis
Chapter 2 begins by describing Intel and a brief history of the location where the research
was preformed, the D2 Factory. Chapter 3 describes the queuing theory behind the
importance of availability variability reductions and the approaches Intel has taken to
identify tools which create variability. Chapter 4 introduces the TAC-TOOT analysis and
presents a semiconductor simulation model which shows why it is important to use the
variability metric rather than the average metric to identify tools which are affecting the
cycle time of downstream tools. Chapter 5 describes the structure and implementation of
the High Precision Maintenance initiative. Chapter 6 discusses how the preventative
maintenance team in Area C used the HPM initiative's 8 key attributes to help it succeed
in reducing variability. The last chapter reviews important observations made in the
previous chapters and makes recommendation which would improve the effectiveness of
the High Precision Analysis.
12
Chapter 2: Intel Background
2.1 Intel
Intel Corporation, an innovator in the silicon industry for over 35 years was founded in
1968 by Bob Noyce and Gordon Moore. With just under 100,000 employees and over
3.8 billion dollars in revenues, the corporation is the worlds largest chip producer. The
industry, in response to increased competition and reduced growth over the past few
years has focused significant efforts on streamlining manufacturing processes. Intel has
created a number of successful corporate initiatives targeting lean manufacturing.
2.2 D2 Factory
D2, located in Santa Clara next to Intel's headquarters was the company's first 200mm
semiconductor factory. It operates both as a production and development facility. While
it has been rumored that Intel may shut down this facility as a result of the high Silicon
Valley operating costs, no steps have been taken to support this theory. Nonetheless, the
topic has motivated both D2 technicians and management to move D2 towards greater
profitability.
The age and location of D2 has forced management to face a number of challenges which
younger factories located in low cost areas have not encountered. Some of these issues
include high cost of labor, aging workforce and factory configuration. Over the past two
decades, Silicon Valley has become one of the most expensive locations to operate a
factory. The cost of the technical labor which operates the facility is significant and the
high demand for experienced technicians in Silicon Valley exacerbates the issue.
Additionally, because D2 is one of the oldest operating semiconductor factories the work
force is very senior. The semiconductor industry is still relatively young so the issues
surrounding a senior work force do not revolve around age or retirement but align more
closely with motivating a work force that is at the top of its salary bracket. The D2
13
management has done an excellent job of managing the expectations of technicians
without the ability to use financial incentives.
The age of the factory plays a significant role in its ability to achieve high volume
production. The limiting factors are the floor space and the disproportional division of
areas to the number of tools in various clusters. This forces the factory to have an
unusual configuration often prohibiting tool groups from being co-located. The division
of tool groups throughout the factory causes issues for efficient WIP routing and
technician allocation.
14
Chapter 3: Variability and Semiconductor Throughput
This chapter describes the queuing theory behind why variability has such significant
effects on WIP and Intel's previous efforts to identify tools which cause variability. The
P-K formula developed by Pollaczek and Khintchine independently in the 1930's uses
queuing theory to explain how variable arrival rates affect WIPI. While Intel
understands the adverse roll which variability plays in affecting factory WIP, it was not
until the late 90's that the company developed the A80 metric to identify tools with
highly variable availability 2 . While the A80 metric is an excellent tool for understanding
individual tool variability, Allen and Capellen developed a model which identifies tools
with variable availability which affected the cycle time of other tools. Their paper,
Improving output capabilitythrough minimizing variability in an HVMfab proposes a
model which uses 3 measures of variability, availability, cycle time and outs, to identify
highly variable tools affecting the throughput of other areas 3
3.1 Pollaczek - Khintchine Formula
The Pollaczek - Khintchine formula, also referenced as the P-K formula, is commonly
used to explain the motivation behind variability reduction efforts in semiconductor
factories4 . The formula describes the average WIP in a queue for a process where the
arrival rate is highly variable and the process time is relatively stable (M/G/1). Previous
research suggests that arrival rates in semiconductor factories resemble an exponential
distribution. The P-K formula is shown below
Heyde, et al., Statisticians of the Centuries (New York : Springer, 2001) 430
C.; Babikian, R., "A80-a new perspective on predictable factory performance",
Advanced
Semiconductor Manufacturing Conference and Workshop, (IEEE/SEMI, 1998) 71:76
3 Allen and Capellen, "Improving output capability through minimizing variability in an HVM fab" (IMEC,
2 Cunningham,
2005)
4 Heyde, et al., Statisticians of the Centuries., 2001 p4 30
15
WIP
=
{p} + {[p 2 ]/[2*(1 - p)]} + {[X 2
]/[2*(1 - p)]I
WIP = average number in queue and in process (units)
A= arrival rate (units per hour)
p = service rate (units per hour)
p =A/ (traffic intensity or utilization)
G2 = variance of service time distribution (0 for constant service times)
The formula can be broken into three segments; tool utilization, effects of increased tool
utilization and arrival variability. The contribution of tool utilization to the average WIP
in the queue is large which is why many factories load their tools well below capacity.
As tool utilization approaches 100 percent, the denominator of the formula's second and
third segments approaches zero driving the WIP towards infinity.
Figure 1 Effects of utilization and arrival rate variability on WIP5
18
14
12
10
84
2
0
0
Of
.5
0.7
0.8
OD
1
Traffic Intensity
1-\r
04
-'kr=
0
&\r=0.8
---
*r
=
1 JO
Additionally, the effects of highly variable arrival rates play a significant role in the
average WIP in a queue. The numerator of the formula's third segment describes how
variability is incorporated into the calculation of average WIP. Often semiconductor
factories choose to reduce WIP by focusing on reducing variability as opposed to
5 Fabtime, "Cycle Time Management for Wafer Fabs", tutorial, http://www.fabtime.com/p-k.shtml
16
increasing capacity. The reduction of arrival rate variability is a change in mindset which
costs a fraction of the price of buying additional tools to increase overall capacity.
The relationship between WIP, utilization and arrival rate variability is described in
figure 1. Reductions in utilization (traffic intensity) through either reduced wafer
releases or increase capacity clearly drives the relationship when capacity is less than
80% utilized. When capacity utilization is greater than 80% arrival rate variability
significantly affects WIP in the process.
3.2 A80 Metric
The A80 metric, first piloted in fab A and later adopted by all 200mm factories is the 80
percent confidence interval of a tool's availability6 . The measure was adopted because
the average availability, denoting the 50% confidence interval was only accurate 50% of
the time that WIP was present at the tool while the A80 measure was accurate 80% of the
time WIP was present at the tool.
The theory behind choosing an 80 percent confidence interval is described by
Cunningham and Babikian in their paper A80-a new perspective on predictablefactory
performance7 . The paper explains that theoretically assuming a normal distribution using
one standard deviation as the lower bound would lead to an 86% confidence interval. A
more robust statistical method would be to use the lower quartile of 25% as the lower end
of a confidence interval. Since the availability distribution varies between a Poisson and
normal distribution, Cunningham and Babikian choose to use 80% because it was in
between 86% and 75%.
D2 is concerned that the A80 is not the appropriate metric to be used by a development
factory. This concern is based on the fact that D2 tools spend a significant amount of
their time not available to production because they are being used for development
Cunningham, C.; Babikian, R., "A80-a new perspective on predictable factory performance",
Advanced
Semiconductor Manufacturing Conference and Workshop, (IEEE/SEMI, 1998) 71:76
7 Cunningham, C.; Babikian, R., "A80-a new perspective on predictable factory performance", Advanced
Semiconductor Manufacturing Conference and Workshop, (IEEE/SEMI, 1998) 71:76
6
17
operations. The method used to calculate A80 measures tools which are not in
production as in repair when in D2 often they are being used for development.
Measuring a tool's availability using the time it is available for production in a
development factory is a false representation of the time the tool is actually available to
process WIP and significantly lowers its A80 metric.
3.3 Intel White Paper - Improving output capability through
minimizing variability
The paper, Improving Output Capability Through Minimizing Variability in an HVM Fab
written by David E. Allen and Rachelle Capellen describes how Intel Fab B maximized
its output capacity by minimizing line variability. The factory used a combination of
tool cycle time, outs and availability variability to identify tools generating line
variability. Cross functional focus teams were then assigned to work with the tool's
equipment engineers and technicians to minimize availability variability.
The holistic method which the factory uses to identify tools generating variability takes
into account both the individual tool's ability to processes WIP on a continuous basis and
the effects of upstream processes on the flow of WIP to the tool. The method evaluates
three metrics, the availability variability, the outs variability and the cycle time
variability. High availability variability is a key indicator that equipment technicians are
having trouble keeping the tool up and running. A tool's outs variability describes the
area's burst capacity. Burst capacity is the ability of a tool to processes high volumes of
WIP in short periods of time. A tool's burst capacity can be used to determine if WIP
bubbles created in upstream are being passed through the tool and affecting processes
further down stream. The cycle time variability describes the variation in the time
between when the WIP arrives at the tool and when the process has been completed.
Cycle time variability can indicate either that the area is prone to WIP bubbles created by
upstream processes or that the variable availability is hindering the area's ability to
processes WIP.
8 Allen and Capellen, "Improving output capability through minimizing variability in an HVM fab" (IMEC,
2005)
18
3.4 Allen & Capellen Variability Model
Allen and Capellen used a unique method to measure variability for cycle time, outs and
availability. The formulas below describe how each variability metric is calculated.
a. Cycle Time Variability
Cycle Time Var. = ( 8 0 h percentile CT -
5 0
h
percentile CT) / ( 5 0 h
percentile CT)
High variability > 15%
b. Outs Time Variability
Outs Var. = (5 0 th percentile outs -
2 0
th
percentile outs) / ( 5 0 th
percentile outs)
High variability > 15%
c. Availability Variability
Avail. Var. = (50th percentile avail. var. - 201h percentile avail. var.) /
(5 0 th percentile Avail. Var.)
High variability > 50%
The reasoning behind the percentile ranges and high variability indicators is not included
in the white paper.9
Figure 2 Variability Example 0
Area
Tool
1
2
2
2
2
3
4
5
5
A
B
C
D
E
F
G
H
I
Cycle Time
variability
49.1%
39.6%
23.1%
43.5%
83.0%
:M8.9%
40.4%
19.0%
Outs
variability
6.7%
5.4%
13.4%
23.1%
26.0%
17.6%
7.7%
8.2%
Availability
variability
7.9%
7.6%
37.1%
8.8%
16.4%
16.6%
5.6%
4.6%
1.3%
9 Allen and Capellen, "Improving output capability through minimizing variability in an HVM fab" (IMEC,
2005)
10 Allen and Capellen, "Improving output capability through minimizing variability in an HVM fab"
(IMEC, 2005)
19
Figure 2, provided by Allen & Capellen demonstrates how Intel Fab B used their
variability analysis to identify tools which affect the cycle time of steps down stream. In
figure 2, tool C with 37.1% availability variability is flagged as having high availability
variability. The influxes of WIP that are being created by tool C are not affecting the
cycle time of Tool D as a result of its large burst capacity. Burst capacity can be
identified through the outs variability. Tools with high outs variability are capable of
processing large amounts of WIP and thus have high burst capacity. Additionally, Allen
& Capellen propose that Tool D's high burst capacity adds to the variability created by
tool C because of it's ability to processes WIP so quickly. Tools E, F and G have
constrained capacity with low burst capacity and can not handle the variability arrival of
WIP which is why the cycle times, 83.0%, 109.3%, and 148.7% are highly variable.
3.5 Conclusion
Intel has understood for over two decades that tool availability variability affects the
throughput time of WIP. The P-K formula, developed in the 1930's is commonly used to
explain the theory behind how variability affects throughput. One of the first metrics
proposed at Intel to identify tool creating variability was the A80 metric. A80 is
calculated for every tool and describes the 80% confidence interval which the tool will be
available to processes WIP. While this is an excellent metric for production factories, it
does not accurately describe development factory tools because the metric does not
account for time tools are taken out of production for development activities. In 2004, a
new model to identify availability variability was pioneered by Allen and Capellen at
Intel Fab B. The method used the variability of cycle time, outs and availably to identify
tools generating variability. Intel Fab B used the analysis to identify areas where focus
teams would work with equipment engineers and technicians to minimize variability.
20
Chapter 4: The TAC-TOOT Variability Analysis
Fab D2 is implementing a variability analysis, TAC-TOOT which identifies highly
variable tools which affect the cycle time of other tools. TAC-TOOT, an acronym for
Tools Affecting Cycle Time Of Other Tools is based on a throughput reduction analysis
preformed at Intel Fab B described by Allen & Capellan' 1 . D2 has deviated from Intel
Fab B's analysis by refining the variability calculation and developing a method which
automates the identification of areas which are the source of high variability. Automation
of the variability analysis will allow other factories to easily implement a TAC-TOOT
analysis and be able to redistribute the man hours that have in the past been utilised to
identify variability towards the reduction of variability.
4.1 Variability Calculation Explanation
The variability metric calculation has been changed from Intel Fab B's normalized
percentile range to an innerquartile range. The innerquartile range is a significantly more
robust measure because it includes the primary range of data points and excludes
outliners. The following sections describe the specific methodology of the variability
calculation for cycle time and availability. The TAC-TOOT analysis does not use outs
variability as a component to identify tools creating disruptive variability. Since it is not
considered a key indicator, the outs variability metric will not be further analyzed.
Cycle Time Variability
Cycle time is the change in time when material is delivered to a tool to when it leaves the
tool. In the case of Intel, cycle time can be explained as the time when a lot arrives at a
stocker where material is stored in a queue before processing until the time when the lot
exits the tool after processing. The distribution of the cycle times for all tools is skewed
"Allen
and Capellen, "Improving output capability through minimizing variability in an HVM fab"
(IMEC, 2005)
21
to the right and bounded by zero. Appendix B shows 14 day cycle time distributions of
35 randomly selected tools within D2.
D2 considered normalizing cycle time variability but found that normalizing variability
would draw incorrect assumptions about the relationship between process time and down
time. Normalizing the variability removes the scale component from a data set.
Removing the scale component assumes that as process time increases so will the
variability of cycle time.
Figure 3 Relationship of cycle time variability with processes time.
Cycle Time Variability v. Process Time
4
9
3.5
9
9
9
3
9
9
9
9
2.5
9
9
(D
2
9
9
9
9
9
E
1.5
1
0.5
9.9
*
*
*9
999
9
1*
*
9
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Process Time, hr
*
Cycle Time Variability -
Linear (Cycle Time Variability) I
Take for instance two tools. Tool A has a process time of 20 minutes with a cycle time
variability of 5 minutes. Tool B has a process time of 100 minutes with a cycle time
variability of 20 minutes. If the cycle times of the two tools were normalized with their
average process time, they would both have the same cycle time variability rank of 0.2.
While both tools do have the same cycle time variability to process time ratio, Tool B's
20 minute variability affects the total WIP process time 400% more than Tool A. The
only reason that might validate normalizing the cycle time data set by average process
22
time would be if the two were correlated. Figure 3 is a graph of the correlation between
processes time and cycle time. The correlation coefficient is 0.31 which indicates that the
two data sets are very weakly correlated and should not be normalised.
Availability Variability
Availability, calculated as a daily percent is the time that a tool is available to processes
WIP. The distribution of the availability is skewed to the left and bounded by 100%.
The variation is calculated using the innerquartile range. Appendix D shows 14 day
cycle time distributions of 11 randomly selected tools at D2. It was determined that
normalizing the availability innerquartile range because it was already a percentage did
not contribute to the availability variability calculation.
4.2 TAC-TOOT Analysis Explanation
The automation of the variability analysis is important because it will allow the analysis
to be preformed more frequently aligning it closely with changes in tool set management.
The analysis correlates tool availability variability with the cycle time variability of down
stream processes. It is important to note that this analysis only identifies tools which
create variability affecting the cycle time of WIP in down stream operations. The
analysis does not identify tools creating variability which only affects the cycle time of
their own process. While it is important to identify all tools creating variability, tools
creating variability which affects other operations have much larger impacts on overall
wafer throughput.
Semiconductor Fab Promodel Simulation
A simulation of a semiconductor manufacturing sequence was analysed to show how tool
availability affects the cycle time of downstream tools. The model is shown in figure 4.
All the tools have a 7 minute process time except for the implant tool which has an 8
minute process time.
The litho process is modeled to a have a normally distributed
down time averaging 10 minutes per hour. The down time standard deviation is adjusted
23
to show how its variability affects the throughput of the entire line. The litho process
availability is calculated as the percentage of time the tool is not available to process
WIP. The table shows that as tool availability variability increases the cycle time of the
total 6 step sequence also increases.
The relationship between the litho tool availability and the implant tool cycle time is
shown in figure 4. The relationship between the average availability and average cycle
time has shallow slope while the relationship between the availability variability and
cycle time variability has a much steeper slope indicating that they are closely correlated.
It is this strong correlation the makes the TAC-TOOT analysis very robust.
Figure 4 Linear Process Example
Thin
Film
Litho
Down
Time STD,
min/hr,
Normal
Dist. (Avg
= 10min)
Avg Down
Time=0
0
5
10
15
20
30
40
-
Planar
-
-N
-
Photo
Lithography
-AEtch
-
-- NImplant
--NDiffusion
Implant
Cycle
Time, min
Diffusion
Cycle
Time, min
Litho Tool
Availability
Line
Throughput
Cycle Time,
min
Var.
Avg.
Var.
Avg.
Var.
Avg.
Var.
Avg.
Var.
7
0
8
0
7
0
100%
0%
43.0
0.0
7
7
7
7
7
7
7
0
0
0
0
0
0
0
8.7
8.6
9.4
10.2
11.6
14.4
21.0
1.0
1.6
2.1
4.0
5.1
8.0
15.0
7
7
7
7
7
7
7
0
0
0
0
0
0
0
83%
84%
83%
81%
78%
74%
68%
0%
11%
23%
30%
37%
42%
58%
45.7
46.3
49.1
55.2
66.5
111.9
195.5
5.0
5.9
10.2
19.1
35.7
93.1
126.5
Thin Film
Cycle time,
min
Planar
Cycle Time,
min
Litho Cycle
Time, min
Etch Cycle
Time, min
Avg.
Var.
Avg.
Var.
Avg.
Var.
Avg.
7
0
7
0
7
0
7
7
7
7
7
7
7
0
0
0
0
0
0
0
7
7
7
7
7
7
7
0
0
0
0
0
0
0
9.0
9.5
11.8
17.0
26.9
69.8
146.5
4.0
4.2
8.0
16.4
33.9
95.1
131.3
Assumptions
1. Cycle time is the total time the material spends at the tool. This is the sum of the time
WIP waits for the tool and the process time
2. Var. = Variability calculated as the innerquartile range
3. Wafer starts (or Arrival rate to thin films) is 1 wafer per 10 minutes
24
Availability v Cycle Time
90%
60%
70%'
6 0%
509/
0430%
20%
10%
0%
0.0
5.0
10.0
15.0
Implant Cycle Time, min
'
Tool Variability -
EL
-
20.0
25.0
Tool Average
Spearman Correlation
The correlation calculation which the TAC-TOOT analysis uses to identify the
relationship between the availability variability and cycle time variability is the Spearman
correlation. The Spearman correlation is a non-parametric measure of correlation which
does not require any assumptions about the relationship between the variables12
-~a
1)
Where:
p = Spearman correlation coefficient
di= Difference between rank order of corresponding values of x and y
n = number of pairs of values
It is important that the relationship calculation not rely on a frequency distribution since
the correlation is drawn from only 6 variability data points. Each variability data point is
12
Hogg, R. V. and Craig, A. T.
Introduction
to Mathematical Statistics, 5th ed. (Macmillan, 1995,) pp. 338 and 400
25
a compilation of 2 weeks of daily averages. The analysis examines 12 weeks of historical
daily availability and cycle time averages.
Spreadsheet Analysis
An excerpt from the spreadsheet variability analysis preformed at D2 is shown in figure
5. The operations have been changed to protect Intel confidential line route information.
The process step number and area associated with each Spearman coefficient have been
listed on the left. Each availability variability data set was correlated with the following
ten cell numbers. Each row in the table explains how the availability variability of a
process is correlated to the following ten cells including its own.
For example, cell f2 11 is the Spearman coefficient between cell 211 diffusion and cell
216 implant. To identify which following cycle time operation the Spearman coefficient
is evaluating, count out the number of cells it is from the initial cell in the row. In the
case of cell f21 1, it is 6 cells from the initial Spearman coefficient cell. To identify
which process cycle times are being correlated, count 6 cells down from cell 211,
counting cell 211 as number 1. The result is cell 216 implant.
The analysis spreadsheet highlights two important indicators; tools which affect multiple
tools downstream and tools which are affected by multiple upstream tools. The first
indicator, tools which affect multiple downstream operations can be best shown with an
example using figure 5. The highlighted cells in row 215 etch indicate that the data set of
availability variability for 215 etch is correlated with cycle time variability of operations
217 implant, 218 implant, 219 implant and 220 etch. Since 217 implant, 218 implant and
219 implant are all process step 5 preformed by different tools, they should be considered
the same operation. Thus the variability created by 215 etch is affecting both implant
process step 5 and etch process step 6.
26
Figure 5 Variability Calculation Example
Cell f211 with a value of 0.95 is the
Spearman coefficient for the availability
variability of diffusion step 211 and the
cycle time variability of implant 216
Spearman Coefficient
Cell
#
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
Area
Litho
Implant
Diffusion
Diffusion
Diffusion
Diffusion
Etch
Etch
Etch
Implant
Implant
Implant
Implant
Etch
Etch
Process
Step
a
b
1
2
3
3
3
3
4
4
4
5
5
5
5
6
0.31
0.66
0.60
0.77
0.34
0.77
0.46
0.11
0.46
0.66
0.03
0.66
0.89
0.03
0.54
0.00
0.00
0.11
0.16
0.38
0.66
0.30
0.14
0.79
0.39
0.79
0.32
0.38
7
0.23
0.14
6
0.
0.0
d
e
f
0.00
0.00
.00
0.00
0.00
6
0.00
0.77
0.41
0.38
0.95
0.82
0.96
0.04
0.41
h
i
0.00
0.77
0.41
0.36
0.20
0.82
0.05
0.77
0.36
0.82
0.20
0.82
0.05
0.50
0.93
0.82
0.20
0.82
0.96
0.96
0.96
0.04
0.04
0.39
0.57
0.43
0.54
0.38
0.61
0.23
0.39
0.66
0.25
0.82
0.20
0.14
0.38
0.61
0.39
0.91
0.91
0.88
0.77
0.70
0.11
0.14
0.38
0.66
0.32
0.1
0.38
0.14
0.04
0.96
0.96
O96
0.96
0.79
0.39
0.43
0.38
0.25
0.79
0.14
0.34
0.39
0.39
0.43
0.34
0.57
0.46
0.80
0.34
0.14
0.61
0.88
0.80
0.68
0.59
0.59
0.70
.
0.05
0.79
0.93
0.91
0.91
0.80
0.77
0.23
0.80
0.80
0.68
0.45
0.80
0.71
0.18
0.18
0.18
0.68
It is not clear why 216 implant is the only tool in process step 5 that was not affected by
the variability created in 215 etch. One possible explanation is that all tools are not used
equally for all process steps. Semiconductor manufacturing is a reentrant process which
means that all of the tools are used for multiple processes steps during manufacturing.
The implant area could be configured so that the tool identified in this table as 216
implant is used primarily for another process step and not process step 5.
The second indicator, tools which are affected by multiple upstream tools is important to
identify because it shows vulnerability in the line design. An example of this is shown in
figure 5. The cycle time variability of tools 217 implant and 218 implant in process step
5 exhibit a correlation between the availability variability of 209 diffusion, 213 etch and
27
215 etch. The probability of tools affected by multiple upstream operations to experience
increased cycle times as a result of WIP bubbles moving through the area is much higher
because there are multiple areas which can cause these WIP bubbles. While it is
important to pursue the standardization of areas which are creating these WIP bubbles, in
situations such as these, quicker results might be achieved by increasing the capacity of
the affected tool.
4.3 Conclusion
The TAC-TOOT analysis identifies tools with highly variable availability that affects the
cycle time of downstream operations. The analysis uses the Spearman coefficient to
identify correlations in the previous 12 weeks of availability and cycle time data. Tools
with high spearman coefficients can be targeted as having variable availability affecting
the cycle time of downstream operations. A Promodel simulation was generated to
demonstrate how availability variability affects the cycle time of downstream operations
in a controlled environment. The analysis shows that slight increases in variability
significantly affect the cycle time of downstream operations.
28
Chapter 5: High Precision Maintenance
The High Precision Maintenance initiative aims at bringing Intel's manufacturing
division to "The Best Maintenance Organization" by the end of 2008. The program
focuses on the improvement of preventative maintenance through standardization,
continuous improvement and organizational accountability. By minimizing the
variability of tool down time, Intel will be able to reduce process cycle times not only in
the target tool but also in other downstream tools. The benefits of the HPM initiative
include increased line throughput and decreased factory WIP.
5.1 Formula 1 Pit Crews
HPM associates its equipment maintenance teams with Formula 1 pit crews. Although
semiconductor tools do not emulate prestigious Formula 1 race cars, the pit crew's values
are similar to those of the HPM initiative. Pit crews are evaluated on the time the car is
in the pit for routine race maintenance just as Intel's maintenance teams are evaluated on
the time a tool is down for preventative maintenance. The pit crew prepares itself before
the car arrives similarly to how the maintenance teams should before taking a tool into
maintenance. Below is a list of the key attributes which the HPM initiative focuses on;
1. Organizational Commitment and Accountability
Managers measure, ask about and value maintenance excellence. HPM is
engrained in business process and an individual equipment maintenance
performance feedback system is in place.
2. Training and Education
All employees are trained on relative HPM methods, tools and processes.
3. Standardization
29
Maintenance performance precision is emphasized across all shifts. PM
activities are preformed using an identical agreed upon method and
sequence.
4. Methodology for Workplace Organization
Everything has a place and everything is in its place.
5. Clean and Defect Free
Tools are kept in like-new conditions.
6. PM Execution Methodology
After a tool is brought into operation, it is not taken out of production until
the next scheduled PM.
7. Data System
Clear indicators are defined, easy to retrieve and tracked by all
stakeholders. Data is valued and drives continuous improvement.
8. Continuous Improvement
All failures are addressed with a countermeasure and root cause analysis.
5.2 Online Tools
Intel corporate has designed a number of online web tools which support the HPM
initiative. This section briefly discusses two of the tools which were particularly helpful
for D2.
Fuzion's PMOS
The PMOS web application provides an online forum for equipment groups to store
information about PM activities. It provides technicians the ability to create preflight
checklist templates, use them during individual PM activities and store comments about
30
the PM for later use. The most valuable portion of this application is the checklist
templates. A checklist template is a list of tools and parts required before a PM is begun.
These centralized checklists which all technicians agree upon and can edit are a great
improvement.
One area where this program could be improved is if it were linked to the online
specifications. Currently if an equipment engineer changes a PM activity, a equipment
technician must also update the PMOS checklist. By tying the two documents together,
this would allow changes in the specifications to seamlessly change the preflight
checklist as well.
Tool Scheduling System
The Tool Scheduling System (TSS) was created to help bring transparency to tool
scheduled and unscheduled down time. The online program gives individuals in all areas
of the factory the ability to see the current and future availability of all tools on the floor.
TSS can be programmed with tool preventative maintenance schedules in addition to
unique downtime upgrades or repairs.
Rebecca Fearing in her 2004 Thesis, Managing PreventativeMaintenance Activities at
Intel Corporationdiscusses the benefits of strengthening communications across
different functional areas by synchronizing preventative maintenance activities". She
explains that starting three preventative maintenance activities simultaneously as opposed
to sequentially can reduce WIP waiting time by 50%.
5.2 Implementation Model at D2
D2's implementation model for the HPM initiative follows Intel's standard process for
program rollouts. Two key senior managers are assigned the responsibility of
disseminating the initiative through the factory. In the case of the HPM initiative at D2,
13 Fearing, Rebecca C., "Managing Preventative Maintenance
Activities at Intel Corporation" (LFM thesis,
2006)
31
these key roles were assigned to two shift managers on alternating day and night shifts
who had over 20 years of experience at Intel. These managers identify equipment
engineers and shift managers from each area to implement the HPM initiative. On going
HPM methodology training sessions are held for the engineers, shift managers and
technicians.
In some areas, Preventative Maintenance (PM) Teams were created. The purpose of
these teams was to create focus groups that used the HPM methodology to improve tool
preventive maintenance quality. These teams typically consisted of a rotating group of
equipment technicians that operated 24 hours for a portion of the work week.
5.3 HPM Organizational Structure
The HPM initiative is structured to interact and leverage many different groups including
software developers, factories and strategic objectives. The core group which represents
the corporate initiative links these areas together by organizing cross functional meetings.
Figure 6 shows the HPM initiative structure.
Figure 6 HPM Initiative Structure
Strategy
HPM Corp.
Initiative
Factories
Web based factory
optimization tools
SPMOS
STSS
Shift
anagers
Equipment
Technicians
32
5.4 Conclusion
The HPM initiative, designed to minimize variability in tool PM activities, uses 8 key
attributes to drive standardization. The program motivates technicians by drawing on
their outside interests through the association of PM teams with Formula 1 pit crews.
The initiative was rolled out by longtime shift managers who drove the training and
creation of area teams.
33
Chapter 6: Preventative Maintenance Team
The Preventative Maintenance (PM) team is a unique group created at D2 to perform
preventative maintenance activities. Initially the team was created as a solution to skilled
equipment technicians shortages. When the team began taking ownership of PM
activities, it became clear that it could add significant value to the HPM effort. The
following sections describe the PM team's organization and structure as well as
challenges it faced.
6.1 Team Organization and Accountability
The PM team schedule is strategically organized to improve communication between the
front end shifts, back end shifts and engineering. Operating 24 hours alternating 3 and 4
days, the shift week starts on Tuesday. Tuesday is half way through the week for the
front end shift and near the beginning of the equipment engineer's standard Monday
through Friday work week. The PM team's week ends on Thursday and alternating
Fridays which is halfway through the back end shift's work week. Figure 7 shows how
the shifts operate over a two week cycle.
Figure 7 D2 Work Schedule
HPM Team
Back End
Front End
Equipment
Engineering
C
r<CD,
0.
CD
SD
Cn
D
At the end of each week, the PM team meets with one member of the area management
and equipment engineers to discuss the previous week's successes, the following week's
34
schedule and changes to PM's which could improve standardization. The commitment
from factory management to support the PM team and hold it accountable for the area's
preventative maintenance will be a key driver in the success of the PM team.
6.2 Flexibility
The diversity of tool knowledge on the PM team gives it the ability to make informed
decisions concerning tools across the entire area. The PM team consists of five
equipment technicians and one equipment engineer. Two of the equipment technicians
work on the night shift, two on day and one trainee that swings between shifts. The
equipment technicians on each shift are skilled on a variety of tools which allows the
team to have 100% utilization.
In the past, skilled equipment technicians were spread over the entire week. Technicians
had to be careful not to start a PM on a shift when there wouldn't be a capable
technicians on the alternating shift to continue PM work. Sometime it was just not
possible to hold a PM till there was a proper spread of technicians across the 24 hour shift
and the tool would sit down for the 12 hour shift when no one was able to continue the
work.
6.3 Training and Education
The PM team enables technician training on both the team's shift and the concurrent
shifts. A new technician must learn both preventative maintenance activities and trouble
shooting. Intel sends many equipment technicians to outside training courses but,
without the routine practice and guidance from senior technicians, it is unrealistic to
expect a new technician to be confident in all PM activities. The PM team is an excellent
training ground for new technicians because it provides technicians with the opportunity
to repeatedly practice PM activities and be mentored by skilled technicians. New
technicians on the standard shifts also have the opportunity to gain experience performing
PM activities during their shift.
35
6.4 PM execution methodology
The PM team's primary goal is to drive continuous improvement and standardization
through PM activities. Just the creation of the team created a significant improvement in
PM activity durations because PM's could be preformed 24 hours as a result of the new
skilled technician configuration. The team continuously worked through the 8 key HPM
initiative attributes described in the previous chapter.
Tool Kits
The ownership and responsibility of tool boxes is one of the largest challenges the PM
team faced. There are a number of large tool boxes which all shifts share. Each drawer
is labeled with the type of tool which belongs in the drawer i.e., US and metric wrenches.
Without further organization, it is difficult to locate specific tools within each drawer.
One step the PM team took was to create tool kits for each type of tool. The kits were
stored in small portable tool boxes and contained 12 to 15 key tools frequently used in
PM activities. The tool boxes had a picture attached to the lid of all the tools assigned to
the tool kit. The team still had difficulty taking ownership of the tool organization.
Foam cutouts for the tool boxes which keep tools in order could benefit the kitting effort.
Parts Clean
Area C has a large number of assemblies which are replaced with clean assemblies during
PM activities. The area which cleans these assemblies, Parts Clean, is located in the sub
fab. The distant proximity of Parts Clean from the Area C forces the two departments to
communicate using beepers, email and telephones. The naming convention for the
assemblies, which is different between Area C and Parts Clean, has in the past generated
confusion.
The PM team and the Parts Clean department worked to alleviate some of these issues by
creating assembly tags which could be used to communicate which Parts Clean
36
technician cleaned the assembly and if there was an issue with the assembly during a PM,
what the issue was and which equipment technician had made the comment. Assigning
responsibility for these assemblies to specific technicians helped to open channels of
communication
The improved communication between the two departments also increase
interdepartmental meeting to discuss the standardization of Parts Clean processes. Parts
Clean was in the process of training new technicians and the feedback from Area C
equipment technicians helped to improve Parts Clean training classes. The root of the
problem for Parts Clean is that the department does not have clear specifications for
training technicians on the proper method for cleaning assemblies. Creation of these
specifications is going to be a very large project but will be extremely beneficial in
reducing defects.
Run the PM
Run the PM, often abbreviated RTPM, is a HPM process preformed by equipment
technicians and equipment engineers to standardize PM activities. Commonly, changes
are made to PM specifications but the sequence of steps in the document are not reevaluated. Just recently, Area C reduced the time it took to take a tool down by
eliminating 50% of the cooldown time. Typically during the cool down, equipment
technicians would collect their tools and replacement parts. Now with the shorter cool
down period, there was not enough time to collect all the tools and parts needed. Some
equipment technicians adjusted and prepared in advance while others still followed the
same process allowing the tool to cool down for longer than necessary. During a RTPM
event, advanced technicians perform a single PM while the remainder of the technicians
and equipment engineers watch. As the technicians step through the specification
sequence document, they evaluate as a group the most efficient way to perform a PM. At
the end of the session, the group agrees on a standardized method to perform the PM and
the specification document is edited to reflect decisions made during the meeting. The
37
RTPM event standardizes the process by which all area equipment technicians perform
the PMs and helps to minimize the variability in time when tools are in PM activities.
6.5 Data Systems
The team maintains spreadsheets which track the preventative maintenance down time
for each type of PM, the availability and A80 for each tool group. The availability and
A80 metrics are a holistic performance indicator for the tool group because they reflect
total tool up time. Total tool up time is affected not only by PM activities but also
unscheduled maintenance and engineering activities. Engineering activities during the 6
month PM team ramp up included elimination of significant amounts of post PM tool
qualifications and reduction in PM activity frequency. Below is the availability and A80
chart for the two tools in Area C which were identified by the D2 HPM initiative as
having significant effects on wafer cycle times and potentially benefiting from the HPM
program. While the two indicators are not great metrics to evaluate the performance of
the PM team, they do show the success of the HPM initiative in the tool groups.
Tool D A80 metrics indicate that the A80 increased by an impressive 9.3% during the 6
month HPM initiative introduction. Tool E increased by a slightly less impressive 5.7%.
Interestingly, while Tool D was able to maintain a relatively constant A80 improvement,
Tool E's A80 metrics is slipping throughout the entire 6 months.
The availability of Tool D and Tool E reflects their A80 metric. Tool D shows a clear
increase of 6.5% in availability which is also characterized by reduced variability. Tool
E shows no increase in the availability during the 6 months after the start of the PM team.
38
Figure 8 PM Progress Tracking
D2 Area C A80 Chart
1.4
Too D increase 9.3%
Tool E increase 5.7*X
1.2-
k 0.
Z
[i- Tool D
I-*-Tool E
0.6 -
0.A
Pre WW25
Tool D Avail Avg 49.5%
Tool E Avail Avg 50.6%
PM Team Start WW25
Tool D Avail Avg 58.8%
Tool E Avail Avg 56.3%
0.2
0
0
10
20
30
Week
40
50
60
D2 Area C Availability Chart
Tool D increase 6.5%
Tool E increase 0%
0.9
0.8
0.75 i0.7
-
-
-
-
-+-Tool0
--
M,/ k IA A_ j AV I
IF, \IlA'O
S--Tool E
V
*
.t
Pre WW25
PM Team Start WW25
Tool 0 Avail Avg 69.3%
Tool E Avail Avg 70.8%
Tool D Avail Avg 628%
Tool E Avail Avg 71.3%
0.55
0
10
20
30
40
50
60
Week
39
Figure 9 Tool PM Down Time Tracking
D2 Area C Tool D PM Down Time
40.00
35.0030.0025.00-
0
x
20.00 15.00 10.00-
5.00-
0.00
F15
,(
,
I'b
Event Duration
QualTime
EngineeringTime
UnScheduledMiscTime
I
I
R
MMMWaitingTechTime
E7O
---
Perf MonTime
OOCTime
Non PM Team
o
V WaitingPartTime
WaitingSuppTime
V7=A utomation Time
TargetG2G Time
IME
RepairTime
FacilitiesTime
ScheduledMiscTime
In addition to tracking each tool group's A80 and availability, the team also tracks how
individual PM's compared to the target down time, other previous PM's and the break
out of logged down time. Above is a chart which shows the Tool D PM F chart. The
grey bars represent PM Fs which were either preformed prior to the PM team or not by
PM team members. The bars broken out into different colors show PM Fs preformed by
the PM team.
One of the key issues which faces the Area C PM team is the low frequency and high
variation of PM activities within the area. The most frequent PM activity occurred every
2 months and there were over 10 different PMs for Tool D and Tool E. It was difficult
for the team to gauge its success on a PM activity when the last PM of that type occurred
over 6 months ago.
40
6.6 Conclusion
The PM team is effectively organized to assign ownership of the PM activities to
equipment technicians on the team. The team's mid week schedule overlaps with the
other shifts to provide both support and improved communication between the shifts.
The team undertook a number of initiatives to improve the day to day operations of Area
C. These initiatives included training of other technicians, creation of tool kits, and
improved relationship with the Parts Clean group. The metrics used to track the team's
success were created and managed by the team. The incentive the team took to improve
the area's operations show the team felt a sense of ownership for the PM activities.
41
Chapter 7: Observations and Recommendations
The HPM initiative is an extremely well designed program that has the ability to reduce
wafer throughput times across the entire factory. The success of the initiative is highly
dependant on the participation of equipment technicians to improve and standardize
preventative maintenance activities. The PM team creates an environment where some of
the factory's most skilled technicians can collaborate to drive standardization through
preventative maintenance activities. Expanding the program throughout the entire D2
factory would help drive the HPM initiative deeper into the factory's culture.
7.1 PM Team Expansion
The PM Team is an excellent use of resources and if proliferated throughout D2 could
improve wafer throughput. Not only does the PM Team focus on implementation of the
HPM initiative but it also recognizes highly skilled equipment technicians. The PM team
program will have the greatest affect if it is first expanded to areas with high availability
variability identified by TAC-TOOT and then throughout the remainder of the factory.
The success of the PM team expansion will depend on three critical components: team
ownership, management support and proper implementation of continuous improvement
metrics.
Team Ownership
The PM team will be most effective if it is given complete ownership of the scheduling
and preventative maintenance activities. This means that the team will still work closely
with equipment engineers and shift managers but the preventative maintenance final
scheduling decisions will be made by the PM team. Giving the PM team the power to
operate the area's preventative maintenance will assign the success of the preventative
maintenance activities to the PM team members.
42
A rotation of the area's advanced equipment technicions through the PM team will allow
all technicians to have a chance to participate. Initially, when the team is first starting,
capable equipment technicians will be asked to participate on the team but, ultimately a
position on the PM team should be sought after and technicians will volunteer to
participate on the team. Having the PM team's weekly shift scheduled during desirable
shifts will help motivate technicians to participate on the team.
As was discusses earlier, many of the skilled equipment technicians at D2 have reached
the top of their pay scale. It has been a challenge for management to continue to satisfy
the career aspirations of these technicians. Offering them the opportunity to participate
on the PM team with complete ownership of the preventive maintenance activities will be
an excellent opportunity for Intel to recognize them as highly valued equipment
technicians.
Management Support
The support of the factory management is the second key component to the PM Team's
success. The collaboration of equipment engineers with the equipment technicians will
enable the PM team to align the preventative maintenance activities with the HPM
initiative. The collaboration of the two groups will bring together the practical
knowledge of the equipment technicians with the theoretical knowledge of the equipment
engineers. Together, the two groups will be able to make significant improvements to
preventative maintenance activities' sequencing and repeatability.
While the key support relationship will be between the equipment engineers and the PM
team, it is important for the equipment technician's direct supervisor to be involved in the
support of the PM team. The supervisors can act to drive continuous improvement as
well as interact with other PM team managers to learn about their team's successes.
Currently, many of the shift supervisors meet at a weekly HPM meeting where they share
their area's progress. In the future, it could be beneficial to have higher equipment
43
technician participation at these meetings because it will allow them to learn directly
from each other and less through third parties.
In order to give the PM team ownership of the scheduling and planning of the
preventative maintenance activities, some responsibility could to be removed from the
FAMs, shift managers and equipment engineers. These groups may resist releasing the
ability to schedule preventative maintenance. One explanation for the change in
responsibilities could be that their core responsibilities are not tied to individual
preventative maintenance activities but to broader scale functionality of the entire area.
The time that they would have previously spent negotiating scheduling can now be used
for supporting the PM team.
Continuous improvement
The implementation of performance metrics will help the PM team drive continuous
improvement. The current metrics, tool average availability and A80 can be modified to
reflect the PM team's individual performance. PM Team metrics could include
" Preventative Maintenance tool down time variability by PM type
" Percentage of Waddington effect (An unscheduled tool downtime attributed to a
poor preventative maintenance)
" Run the PMs performed each month
There are already some web tools which track these metrics for each area. Unfortunately
many of the company wide web tools can not be customized for individual areas.
Whether the teams use a web tool or spreadsheet, it is important to gaining consensus on
these metrics from each area.
7.3 Future Areas of Research
The metrics used to gauge the success of each PM team need additional development and
validation. Part of what makes the PM team metrics difficult is that each area has
44
different challenges. Capturing all of these challenges in one set of metrics which is
valuable to every area will be important.
7.4 Conclusion
The PM team program adds tremendous value to the HPM effort. It utilizes the
knowledge of experienced equipment technicians to help standardize preventative
maintenance activities. The expansion of the program to the entirefactory could help all
areas to strengthen their HPM effort. Three key components to the team's success are
team ownership, management support and continuous improvement. Allowing the PM
team to own the preventive maintenance activities will help assign responsibility of the
success of each activity to the team. The management support of the PM team will
empower the team to improve standardization of preventative maintenance activities.
One area of future research, critical to the team's success, is the development of
performance metrics to drive continuous improvement.
45
References
Allen, David E. and Capellen, Rachelle, "Improving output capability through
minimizing variability in an HVM fab", IMEC, 2005
Cunningham, C.; Babikian, R., "A80-a new perspective on predictable factory
performance", Advanced Semiconductor Manufacturing Conference and Workshop,
1998, IEEE/SEMI, 23-25 Sept. 1998 Page(s):71 - 76
Fabtime, "Cycle Time Management for Wafer Fabs", tutorial, http://www.fabtime.com/pk.shtml
Fearing, Rebecca C., "Managing Preventative Maintenance Activities at Intel
Corporation", LFM thesis, 2006
Heyde, E. Seneta, editors ; P. Cr6pel, S.E. Fienberg, J. Gani, associate editors,
Statisticians of the Centuries. New York: Springer, 2001 p4 3 0
Hogg, R. V. and Craig, A. T. Introduction to Mathematical Statistics, 5th ed. New York:
Macmillan, 1995, pp. 338 and 400
46
Appendix A: Cycle time distribution of 28 Average Daily
Periods for 35 randomly selected processes
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--
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Column 2
Momen"s
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Column)3
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upper 95% Mean
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lower 95% Mean 0.9265934
N
28
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N
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0.74774
0.70218
0.62207
0.56928
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SidDay
0.0632017
0.9743975
0.0351016
0.0066336
0.9880086
0.9607865
28
0.8840
Quamntiles
maximum
Mean
Sid Dev
Std Err Mean
upper 95% Mean
lower 95% Mean
N
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CaQu~nillee
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Mean
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Sid Dev
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0.0181423
Sd Err Mean
upper 95% Mean 0.9663179
lower 95% Mean 0.0918679
N
28
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0.996
10.0%
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99.5%
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0.9737125
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Std Err Mean
upper 95% Mean
0.9331922
0.0605748
Std Err Mean
0.0114475
upper 95% Mean 0.9566807
975%
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Moments
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0.942883
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10.0%
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upper 95% Mean
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0.7626
0.7626
0.7826
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90.0%
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lower 95% Mean
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0.9655
0.9779484
0.0187114
0.003531
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an
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upper 95% Mean 0.7601575
lower95%Mean 0.7111434
N
28
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100.0% maximum 0.99875
99.5%
0.99875
97.5%
0.99875
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50.0%
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25.0%
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10.0%
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0.62555
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