Identification and Mitigation of Performance-Limiting Defects
in Epitaxially Grown Kerfless Silicon for Solar Cells
by
Douglas M. Powell
B.S. Mechanical Engineering, The Ohio State University, 2008
S.M. Mechanical Engineering, Massachusetts Institute of Technology, 2012
Submitted to the Department of Mechanical Engineering
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy in Mechanical Engineering
MASSACHUSETTS INSTITUTE
at the
OF TECHNOLOGY
AUG 1 5 2014
LiSR I
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2014
2014 Massachusetts Institute of Technology. All rights reserved.
Signature redacted
Author...................................................
Department of M
anical Engineering
May 21, 2014
Signature redacted
Certified b ....................
Tonio Buonassisi
Assistant Professor of Mechanical Engineering
Thesis Supervisor
Signature redacted
A ccepted by ............................................
......................................
David E. Hardt
Professor of Mechanical Engineering
Chairman, Department Committee on Graduate Theses
Identification and Mitigation of Performance-Limiting Defects in Epitaxially
Grown Kerfless Silicon for Solar Cells
by
Douglas M. Powell
Submitted to the Department of Mechanical Engineering
on May 22, 2014 in Partial Fulfillment of the
Requirements for the degree of Doctor of Philosophy in
Mechanical Engineering
ABSTRACT
Reducing material use is a major driver for cost reduction of crystalline silicon photovoltaic
modules. The dominant wafer fabrication process employed in the industry today, ingot casting
& sawing, wastes approximately half of the input silicon feedstock due to sawdust (kerf) and
ingot tailings. Alternative "kerfless" wafer-fabrication technologies avoid ingot casting and
sawing, and can reduce the amount of silicon feedstock used by over 5x. But for kerfless silicon
to be cost effective, wafers must be of high electrical quality, as the power conversion efficiency
of a photovoltaic module is the strongest determinant of manufacturing cost. Epitaxially grown
kerfless silicon has the potential to both significantly reduce silicon waste, and provide sufficient
electrical quality to support high-efficiency photovoltaic modules because of its single-crystal
structure and low structural defect density.
The goals of this research effort are to identify the root cause of underperformance of
epitaxial wafers, to develop defect-mitigation strategies, and to translate these to electronic
performance improvements. Low carrier lifetimes, a measure for electrical performance, of < 1
is were observed in as-grown epitaxial material. Injection level dependent lifetime, bulk mass
spectrometry (ICP-MS), deep level transient spectrometry (DLTS), and micro x-ray fluorescence
(I-XRF) measurements were completed to identify underlying performance-limiting defects. The
comparison of the defect concentrations obtained in the above measurements to Shockley-ReadHall (SRH) recombination modeling indicates that a getterable impurity contaminant is the
primary performance limiting defect in p-type as-grown material.
3
The gettering response of p-type epitaxial material is assessed for two generations of wafer
growth equipment. In the first generation of material, gettered lifetimes were < 20 ps at an
injection level of 1015 cm- 3 . While a significant improvement over as-grown performance, this
performance level is inadequate for high-efficiency photovoltaics. A second generation of
material was grown in an upgraded environment with improved impurity management and
greater automation. The effective lifetime of the second generation of material improved over
lOx. With the application of standard and extended phosphorus diffusion gettering processes,
effective lifetimes of over 300 ps and estimated bulk lifetimes over 800 ps are achieved in < 100
pm thick p-type epitaxial wafers at an injection level of 1015 cm-3 with highly effective A1 2 0 3
surface passivation. Very low concentrations of interstitial iron, a dominant defect in p-type c-Si,
were achieved after standard (2.5 0.76)x10 10 cm-3 and extended (3.2 7.4)x109 cm- 3 gettering
processes. With bulk mass spectrometry and SRH recombination modeling, the source for the
lifetime change between generations is identified as a reduction in contamination of difficult to
getter impurities including Mo and V. Other potential performance limiting defects, including
interstitial iron contamination and structural defect density, are not observed to change between
generations.
Both the excellent gettering response and low as-grown lifetime of the material is attributed
to the rapid cooling and low density of heterogeneous nucleation sites in as-grown material. This
leads to a large concentration of point-defects, a more recombination active configuration than
precipitates, in as-grown material. Controlled pre-gettering annealing experiments of as-grown
material showed that material performance after gettering can be reduced with pre-gettering
annealing.
Similarly, as-grown and gettered material was assessed for n-type epitaxial wafers. As-grown
lifetime is again low, < 1 ps, but improves to over 800 ps (effective) at an injection level of 1015
cm-3. In the as-grown state, a getterable impurity is indicated to limit lifetime. The effective
lifetime of over 800 ps at an injection level of 1015 cm-3 of the n-type material results in a
.
calculated surface passivation effectiveness that matches the best results obtained with A1 2 0 3
This indicates that no unintended contaminant is responsible for the performance of the wafer.
In conclusion, very high effective lifetimes, eclipsing values achieved with historic kerfless
silicon growth technologies, are demonstrated with epitaxial kerfless silicon for solar cells after
gettering. The achieved lifetimes are capable of supporting high-efficiency device architecture.
Using defect science, the underlying causes for underperformance of as-grown material and early
generations of growth are indicated.
Thesis Supervisor: Tonio Buonassisi
Assistant Professor of Mechanical Engineering
4
ACKNOWLEDGEMENTS
I have started to get a clearer picture of the privileges I have enjoyed as a child, student,
company employee, and student (again). This is the case on both international and local levels. I
have spent a decent amount of time stressed out while I have been at MIT. A lot of this is
because I am a big procrastinator and easily get excited about new things. In moments of
particularly high pressure, I would find myself desiring an "easier" situation. There was a hefty
dose of elitism in these thoughts, but I have started to appreciate how lucky I am in the
framework of Maslow's Hierarchy of Needs. 1' 2 In short, you can't work on long-term goals like
developing renewable energy technologies, without having a Maslow's Hierarchy of Needs
solid foundation that provides food, water, sanitation, safety,
etc. It indeed is not "easy" to be in a situation where these Self-Actualization Lon erm
fundamental needs are the primary focus. I think it is fair to say
that being a graduate student is in the "long-term" category near Esteem
the top of the pyramid. It is a privilege to have an opportunity to
work on such endeavors. Therefore, there are quite a lot of Love
people (and environmental factors) that have played a pivotal
role in enabling me to complete this thesis. The group is safety
expansive, ranging from my elementary school teachers to
anonymous (to me) and courageous public servants, as well as Physiological
family and friends. Some people are listed below.
A few things have changed in my life since I wrote the acknowledgements in my Master's
Thesis. For one, Ellen, who I acknowledged in that document, will be marrying me in a few
days. I think that is a decent prize for finishing my thesis (. Otherwise, my non-academic
acknowledgements are the same here: God, Ellen, Mom, Dad, and Joe.
There are also a few people who enabled for me to attend graduate school in the first place.
First, it was Ellen who suggested that I apply. Then, S.A. Korpela, G.L. Kinzel, and B. Smalley,
all wrote me letters of recommendation with only a few days' notice. Then unknown to me, a
former PV lab member, S. Hudelson, was my behind-the-scenes advocate to get me into the lab.
Once I was here, I have benefited from the valuable contributions of many advisors and
collaborators. First off there is Tonio. I think our shared excitement to work on new problems
has made for a fast-paced and rewarding collaboration. Additionally, I acknowledge my
committee members of Professors Hardt and Parks. Then there are all the other students and
post-docs in the PV lab. These include: J. Hofstetter, S. Castellanos, D.P. Fenning, A.E.
Morishige, M.M. Kivambe, M. Ann Jensen, D. Berney Needleman, M.T. Winkler, C.B.
Simmons, J.T. Sullivan, H.J. Choi, R.E. Brandt, R. Chakraborty, K. Hartman, V. Steinmann,
M.A. Lloyd, J.Z. Lee, M.L. Castillo, J. Serdy, A.R. Nguyen, A. Akey, S.M. Scott, and M.A.
Bertoni. Additionally, many people outside of MIT have made significant contributions. These
include: V.P. Markevich, A.R. Peaker, T.S. Ravi, R. Hao, S. Hegedus, K.V. Ravi, A.C.
Goodrich, T.L. James, M. Woodhouse, J.D. Murphy, H. Savin, W.M.M. Kessels, D. Macdonald,
J.E. Cotter, G. Wilson, Z. Shu, and J. Cotsell. Lastly, there are many resources that I have
employed. There is of course L. Regan, who keeps a good deal of the foundation to the pyramid
above together. The cleanroom facilities at Harvard CNS and the machine shop at the LMP have
also been important parts of time at MIT. Lastly, I acknowledge the taxpayers and policy makers.
The majority of my work has been funded through U.S. DOE contracts. Additionally, I have
received the benefit of the U.S. DOD NDSEG fellowship program.
5
TABLE OF CONTENTS
Abstract..........................................................................................................................................
3
Acknow ledgem ents .......................................................................................................................
5
Table of Contents..........................................................................................................................
6
List of Figures................................................................................................................................
9
List of Tables ...............................................................................................................................
11
1.Photovoltaic Innovation Opportunities ...............................................................................
12
1.1
Benefits of Photovoltaic Deploym ent........................................................................
12
1.2
Barriers to Photovoltaic Deploym ent.........................................................................
12
1.3
Technology Introduction...........................................................................................
14
1.4
PV Innovation Opportunities......................................................................................
15
1.4.1
Opportunity Assessment with Technical Cost Models ..........................................
17
1.4.2
Minim um Sustainable Price Sensitivity Analysis .....................................................
18
1.4.3
Enabling Local M anufacturing.............................................................................
21
M aterial Requirem ents for High-Efficiency Cells....................................................
22
1.5
1.5.1
1.6
Impact of Miniscule Defect Concentrations in Determining Lifetime ................. 23
Thesis Overview .......................................................................................................
2. Epitaxially G rown K erfless Silicon ...................................................................................
24
25
2.1
Standard c-Si PV M odule M anufacturing..................................................................
25
2.2
Kerfless Silicon Technologies ...................................................................................
26
2.3
Kerfless Epitaxy M anufacturing ...............................................................................
28
3. Lifetim e-Lim iting Defects ...................................................................................................
31
3.1
Intrinsic Lifetim e Limits............................................................................................
31
3.2
Lifetim e Lim its from Avoidable Defects......................................................................
32
6
3.2.1
Shockley-Read-Hall Recom bination M odel ..........................................................
32
3.2.2
Impurity Point Defects ..........................................................................................
34
3.2.3
Structural Defects...................................................................................................
38
4. Lifetim e M easurem ents......................................................................................................
41
4.1
Surface Lifetime Lim its.............................................................................................
41
4.2
Injection Level Dependent Lifetim e M easurem ents..................................................
42
4.2.1
M easurem ent Noise...............................................................................................
43
4.2.2
Impact of Room Illumination....................................................................................
45
4.2.3
Impact of Flash Lam p Head Height ......................................................................
46
4.2.4
M inim um Sam ple Thickness.....................................................................................
47
4.2.5 Reference Cell Aperture........................................................................................
47
4.2.6
Optical Constant....................................................................................................
48
4.2.7
W CT-120 Lifetim e Comparison ............................................................................
49
4.3
t-PCD Lifetim e Mapping ..........................................................................................
49
4.4
Interstitial Iron Measurem ent....................................................................................
51
4.4.1
Pre-factor...................................................................................................................
52
4.4.2
Fei-B, Dissociation.................................................................................................
54
4.4.3
Fei-Bs A ssociation Tim e........................................................................................
56
4.4.4
M easurem ent Process.............................................................................................
57
5. Surface Passivation.................................................................................................................
59
5.1
A LD Alum inum Oxide .................................................................................................
61
5.2
M easuring Passivation Effectiveness........................................................................
63
5.3
Corona Charge of Surface Passivation .....................................................................
65
6. Phosphorus Diffusion Gettering ........................................................................................
66
6.1
Internal vs. External Gettering....................................................................................
66
6.2
Phosphorous Diffusion Gettering ..............................................................................
67
6.3
Getterability of Impurities...........................................................................................
71
6.4
Furnace M aintenance .................................................................................................
72
7. Defect Identification in As-Grown Epitaxial Wafers ......................................................
75
7.1
A s-Grown Lifetim e and Interstitial Iron Concentration ............................................
75
7.2
Bulk M ass Spectrom etry.............................................................................................
76
7.3
Synchrotron m icro x-ray fluorescence......................................................................
77
7.4
Oxygen..........................................................................................................................
80
7
7.5
Response to Gettering and High-Temperature Annealing........................................
80
7.6
Deep Level Transient Spectrometry ..........................................................................
81
7.7
As-Grown Performance Limit Assignment ...............................................................
82
7.8
Defect Distribution in As-Grown Epitaxial W afers.................................................
83
7.9
Contamination Source...............................................................................................
88
8. Gettering Response.................................................................................................................
90
8.1
M aterial.........................................................................................................................
90
8.2
Lifetime Improvement with Gettering......................................................................
90
8.2.1
Phosphorus Diffusion Time Temperature Profiles...............................................
91
8.2.2
Lifetime and Iron Contamination after Gettering .................................................
91
8.3
Impact of Impurity Distribution on Gettering Response ..........................................
96
8.4
Root Cause of Lifetime Change between Generations...............................................
100
9. Defect Identification and M itigation in n-type M aterial...................................................
105
9.1
As-Grown Lifetime and Defect Identification............................................................
105
9.2
Lifetime Improvement with Gettering........................................................................
108
9.2.1
Comparison to Intrinsic Limits ...............................................................................
110
9.2.2
p-XRF M easurement of Gettered n-type Material..................................................
111
10. Conclusions..........................................................................................................................
10.1
Conclusions.................................................................................................................
114
114
References..................................................................................................................................
116
Appendix A: Cost M odeling Resources ..................................................................................
126
Appendix B: Gettering Standard Operating Procedure.......................................................
127
Appendix C: Emitter Removal & W afer Thinning with CP4 ..............................................
132
Appendix D: Atomic Layer Deposition Standard Operating Procedure ............................
134
8
LIST OF FIGURES
Figure 1.1: "Win-Win" Innovation Opportunities......................................................................
16
Figure 1.2: Cost Modeling Methodology..................................................................................
18
Figure 1.3: Price Sensitivity Map for Standard mc-Si PV Manufacturing ...............................
19
Figure 1.4: Impact of Module Efficiency on Module and System Prices..................................
20
Figure 1.5: Innovation Enables Price Parity ............................................................................
22
Figure 1.6: Lifetime Requirements of High-Efficiency Cells ...................................................
23
Figure 2.1: Kerfless Wafer Methods..........................................................................................
27
Figure 2.2: Schematic of Epitaxial Wafer Preparation.............................................................
29
Figure 2.3: Epitaxial Sample after Exfoliation ..........................................................................
30
Figure 3.1: Intrinsic Lifetime Limits .......................................................................................
32
Figure 3.2: SRH Point Defect Lifetimes at Fixed Injection Level ............................................
35
Figure 3.3: SRH and Intrinsic Lifetime at 1 Sun at the Maximum Power Point ......................
37
Figure 3.4: Simulated Cell Efficiency and Point-Defect Concentration...................................
38
Figure 3.5: Lifetime Impact of Structural Defects...................................................................
40
Figure 4.1: Bulk Lifetime vs. Measured Effective Lifetime......................................................
42
Figure 4.2: Reduction of Measurement Noise with Averaging .................................................
44
Figure 4.3: Number of Points for Time Derivative Noise Comparison....................................
45
Figure 4.4: Impact of Room Lighting on Effective Lifetime....................................................
46
Figure 4.5: Optical Constant for QSS and Generalized Measurements....................................
49
Figure 4.6: p-PCD Map of Float Zone Sample..........................................................................
51
Figure 4.7: Injection Level Dependent Lifetime of Metastable Iron Defect .............................
52
Figure 4.8: Pre-factor for Iron Calculation ..............................................................................
54
Figure 4.9: Fei-B, Pair Defect Dissociation..............................................................................
56
Figure 4.10: Fei-Bs Pair Defect Association Time...................................................................
57
Figure 4.11: Iron Boron Pair Dissociation Experimental Schematic........................................
58
Figure 5.1: Surface-Limited Lifetime........................................................................................
60
Figure 5.2: Pressure Decay inside ALD Reaction Chamber....................................................
62
Figure 5.3: Lifetime and SRV of Float Zone Control Wafer....................................................
64
9
Figure 5.4: Lifetime Improvement with Corona Charging of n-type Samples..........................
65
Figure 6.1: Evolution of Metal Impurities during Phosphorus Diffusion Gettering................. 68
Figure 6.2: Gettering Time-Temperature Profiles ...................................................................
69
Figure 6.3: Calculated Phosphorus Diffusion Doping Profiles .................................................
70
Figure 6.4: Diffusivity and Solubility of Metal Impurities........................................................
72
Figure 6.5: POCl 3 Furnace Contamination before Cleaning .....................................................
74
Figure 7.1: As-Grown Lifetime p-type Material......................................................................
76
Figure 7.2: Synchrotron X-Ray Florescence Analysis of As-Grownp-type Material ............. 79
Figure 7.3: DLTS Spectra for As-grown p-type Material........................................................
81
Figure 7.4: Evolution of Impurities during Wafer Growth........................................................
84
Figure 7.5: Epitaxial Growth Iron Precipitation Simulation..............................
87
Figure 7.6: As-Grown Iron Distribution as a Function of the Wafer Cooling Rate .................
88
Figure 8.1: Injection Level Dependent Lifetime for Gen I1p-type Material.............................
94
Figure 8.2: Performance Limits of Interstitial Iron....................................................................
95
Figure 8.3: DLTS Spectra of As-Grown and Gettered Gen II p-type Material........................ 96
Figure 8.4: Schematic Gettering Comparison of Epi and mc-Si Materials...............................
97
Figure 8.5: Impact of Pre-Annealing on Gettered Lifetime......................................................
99
Figure 8.6: Lifetime in p-type Material after High Temperature Anneal without POCl 3 ......
100
.....
Figure 8.7: Comparison of Structural Defects in Std. mc-Si and Epi Silicon ............................
102
Figure 8.8: Reduction in Impurity Contamination in Gen II Material........................................
103
Figure 8.9: SRH Modelling of Gen I Material after Gettering ...................................................
104
Figure 9.1: As-Grown Lifetime n-type Material.........................................................................
106
Figure 9.2: DLTS Spectra for As-grown n-type Material..........................................................
107
Figure 9.3: DLTS Spectra after Standard Gettering of n-type Material .....................................
109
Figure 9.4: Injection Level Dependent Lifetime of Gettered n-type Material............................
110
Figure 9.5: Intrinsic Lifetime Limit n-type Material..................................................................
111
Figure 9.6: Synchrotron X-Ray Florescence Analysis of Gettered n-type Material................... 113
10
LIST OF TABLES
Table 4.1: Reference Cell Aperture Procedure .........................................................................
48
Table 8.1: Summary of Lifetime Results for p-type Material after Gettering ..........................
92
Table 8.2: Summary of Lifetime Results for Pre-Annealed p-type Material after Gettering ....... 98
Table 8.3: Lifetime Parameters for Gen I and Gen II p-type Material .......................................
101
11
CHAPTER
1
PHOTOVOLTAIC INNOVATION
OPPORTUNITIES
1.1 Benefits of Photovoltaic Deployment
Solar photovoltaics (PV) are a low-carbon renewable electricity generation technology that
harness the power of sunlight with little interaction with the local environment. The amount of
capturable power from the sun is abundant, and far exceeds human energy demands by a factor
of well over L,000x.3 Additionally, while the intensity of sunlight varies over the world,4 it is
less geographically discriminatory than traditional fossil-fuel energy sources.5
The deployment of PV technology can help address the imperative challenges facing society
today of climate-change6 , 7 and energy supply.8' 9 Among these challenges, are the 1.3 billion
people throughout the world who do not currently have access to reliable electricity and the
benefits it provides. 10' " The relatively transportable form-factor of PV modules and low
maintenance requirements are potentially advantageous attributes for the electrification of this
overwhelming demographic.
1.2 Barriers to Photovoltaic Deployment
In spite of the advantages of PV technology deployment, the share of electricity produced by
PV is small both globally12 and in the U.S. 1 3 Technical factors related to the intermittency of the
12
solar resource and grid integration provide barriers to PV deployment. However, the price of
electricity produced from PV technology, although falling at a rapid pace, 4 '
15
provides a
significant barrier to the widespread deployment of PV technology. Two particular market
failures contribute to this situation: the non-monetization of externalities (i.e., carbon emissions)
of traditional electricity generation technologies, 16 and the fact that innovators do not capture all
of the value they create with an new technology (spillover).
6
Incentives are designed to counteract these barriers and include funding for research and
development (technology push) and subsidizing technology that may not be financially
competitive on its own (demand pull).1 6'
17
Deployment subsidies aim to capture the value of
incremental learning and economies of scale. 14'
18
However, inappropriate subsidization of an
inferior technology can lock-in its use and provide significant barriers to the development of
other superior technology.1 6 Technology push instruments, including R&D funding, is an
alternative approach to encourage cost reductions through technology development. R&D
funding, although more volatile than demand pull instruments' 7 may reduce the total cost of
achieving a policy goal' 9 and provide an improved political reception due to the positive
spillovers created R&D.1 6 However, great effort may be employed to avoid "picking winners"
and crowding-out a superior technologies with R&D funding.16,20
A blended approach that includes both demand pull and technology push instruments is
recommended to combat the distinct market failures of externalities and spillover.1 6 However,
the blend must be optimized to avoid spending too much money on technologies that cannot yet
'
benefit from scale, or are inferior to alternatives. 2
The U.S. Department of Energy supports R&D for a wide variety of PV technologies with the
SunShot program. 2 The program targets subsidy-free cost-competitive electricity generation
13
($/kWh) with PV technology by 2020.
The installed price goals for PV technology are $1 .00/W
for utility-scale customers and $1.50/W for residential customers (in 2010 US$). 2 2 The prices of
PV modules themselves are targeted to reach $0.50/W to achieve the above system price goals,
with the balance of the price distributed amongst other system components. Significant cost
reductions are required for PV module manufacturing to sustainably achieve the $0.50/W
module price goal.
1.3 Technology Introduction
A PV system consists of multiple subcomponents. PV modules are installed on a mounting
structure and wired together. This circuit is connected to a power inverter that allows the PV
system to power the electrical grid when PV production is high, and the consumption of power
when PV production is low. The PV module and inverter represent distinct subcomponents of the
system, but significant costs are present in the other components of the system. This includes
mounting structure and wires, but also labor and overhead costs from installation and permitting.
These additional costs are defined as the balance of system (BOS) and currently represent a
dominant share of the price of a residential PV system. 23 The PV module is an assembly of
multiple PV cells encased in a weather-tight enclosure. PV devices can be produced with
multiple materials, however, crystalline silicon (c-Si) holds a dominant position with over 85%
market share.24
14
1.4 PV Innovation Opportunities
Profit is equal to the selling price of a product minus the cost required to produce it (Figure
1.1, a).2 A manufacturer can increase profit by either increasing prices (Figure 1.1, b) or
decreasing costs (Figure 1.1, c). However, most manufacturers cannot increase prices freely, as
they operate in a competitive marketplace with various alternatives.
26
should create additional value for a consumer to justify a price increase.
Therefore, a product
Reducing the cost
required to manufacture a product is another strategy to increase profit. However, some, all, or
none of manufacturing cost reductions may be passed on to the consumer through a change in
product price depending on the state of the industry.
Innovations in photovoltaic technologies may provide a price premium or reduce cost. An
innovative PV module architecture that increases reliability for example, may allow the
manufacturer to charge a price premium.2 8 The specific amount of this premium could be
determined by understanding how much value the innovation creates for the customer (i.e., lower
maintenance costs).2 7 On the other hand, an innovative technique for reducing the amount of
silver paste needed to manufacture a PV module with the same power output represents an
approach to reduce costs. 2 8
15
b) A Profit
a) Baseline
Profit
c) A Profit
Cost
Cost
Target
Reductions
Cost
Price
Price
Price
Premium
Target
Figure 1.1: "Win-Win" Innovation Opportunities
Profit is equal to price minus the cost to make a product (a). While profit can be
increased by either increasing prices (b) or reducing costs (c), some promising
innovations provide the capability to congruently reduce manufacturing costs and
command a price premium.2
The most promising innovations however, may provide both a price premium and cost
reduction at the same time - a "win-win" opportunity for a manufacturer (Figure 1.1, b and c
together). Innovations are available within the dominant crystalline silicon material system that
accomplish this objective.2 8 3- 0 Increases in the power conversion efficiency of a module provide
this opportunity. Increasing efficiency reduces the amount of labor and land needed for
installation for a system,'2 3'31 which creates value for customers and enables a price premium to
be charged. At the same time, increasing efficiency without impacting other factors reduces
manufacturing costs by decreasing the amount of materials (wafers, glass, etc.) needed to
manufacture a module.29 ' 3 2
16
1.4.1 Opportunity Assessment with Technical Cost Models
The magnitude of an innovation opportunity can be assessed with a technical cost model.30' 32a
The model includes a technology-driven calculation of the cost to manufacture a PV module
coupled to a financial calculation that determines the minimum price that could be charged while
maintaining financial sustainability.3 0 ,
2
As shown in Figure 1.2, the cost calculation includes
accounting for the direct materials, direct labor, and manufacturing overhead that is required to
produce a PV module.
The required levels of these resources are calculated considering
technical (e.g., geometry and throughput), manufacturing (e.g., yield and uptime), and financial
(e.g., depreciation) parameters. 3 2 The minimum sustainable price (MSP), defining long-term
financial sustainable pricing, is then calculated using a discounted cash flow model (Figure
1.2).32 A technical cost model can therefore be used to calculate the potential financial impact of
innovations that either aim to provide an incremental improvement or disrupt incumbent
technologies. It is emphasized though, that analyses should note that market prices change over
time and serve as a moving target.3 ' This is especially the case for disruptive technologies.
a Useful
resources for developing technical cost models are provided in Appendix A.
17
Cost
Accounting Exercise
* Direct Material
- Direct Labor
* Manufacturing Overhead
Price
* Discounted cash flow
* Minimum sustainable price
(MSP) when IRR = WACC
0
M
Tirne
Figure 1.2: Cost Modeling Methodology
A technical cost model is employed to both calculate the cost of manufacturing a
PV module and the minimum price that can be charged for the module while
providing long-term financial sustainability.302
1.4.2 Minimum Sustainable Price Sensitivity Analysis
Opportunities to reduce the minimum sustainable price were assessed with a technical cost
model. Figure 1.3 shows the sensitivity and maximum potential savings of various innovation
areas on the MSP of a standard mc-Si PV module. 2 9, 32 Four groups of influence are shown. The
factors shown in red have the largest influence, while the factors shown in blue have an
intermediate influence and the factors shown in green have less influence. Financial factors,
shown in yellow, also provide a useful price reduction opportunity. Power conversion efficiency
provides the highest potential to reduce MSP, as all area-dependent manufacturing costs scale
with efficiency. 29 , 32 Furthermore, efficiency still provides a significant absolute price reduction
opportunity, as module efficiencies are still well below theoretical limits. 3 3 The manufacturing
parameters of yield and uptime also provide an interesting relationship in the analysis. While
little room for improvement is available in these areas because they are already quite high, costs
18
increases can occur quickly through a reduction in either of these parameters. Silicon feedstock
is highlighted as the most expensive material cost for c-Si PV manufacturing and provides a
valuable cost reduction opportunity. Lastly, among the most influential factors, the capital cost,
or cost required to establish a manufacturing facility, also provides a valuable cost reduction
opportunity. Reductions in silicon feedstock use and capital cost however, must not come at the
expense of module efficiency. 34
Price Sensitivity Map for Standard mc-Si
.a1.00
eldEfficiency
1.0
Uptime
> CD
C
Capital Cost
Silicon *
*
C..
40
Labor
SG&A
C 10
.0ccc
010WACC
WACMetal Paste
Other Module Cost Components:
8) Ribbon
1) Frame
9) JB and Cable
2) Electricity
10) Chemicals
Glass
11) Wire
4) Backsheet
5) Maintenance 12) Crucible
6) Encapsulant 13) Screens
7) Slurry
14) Packaging
40
0
.3)
0. Tax Rate
V
0
E
e
R&D
0.01
0.00
0.25
0.50
0.75
Maximum Price Savings Available [US$W]
Figure 1.3: Price Sensitivity Map for Standard mc-Si PV Manufacturing
Price sensitivity and maximum absolute savings available for various innovation
areas. Better innovation opportunities have both a high sensitivity and maximum
absolute savings available (upper right). Efficiency provides the strongest
influence to reduce price, while other factors play a less significant role. Figure
reprinted from.3 2
While module efficiency provides a large impact on module prices, it also has a significant
influence on installed systems, as many system costs scale with area ($/m2
2
3,
31,
" Figure 1.4
shows the price for a generic PV module (blue curve) and PV systems (green and red curves) as
19
a function of efficiency assuming that the c-Si wafer is free. This method allows a minimum
viable efficiency to be calculated for disruptive technologies by comparing the module price of
an incumbent mc-Si module while assuming that all PV technologies have comparable costs of
cell fabrication and module manufacturing.35 While considering only the module, a minimum
viable efficiency of 9% is required to compete with incumbent silicon technology. However, the
minimum viable efficiency climbs to >11% as the entire PV system is considered, and increases
as the price of the balance of systems (BOS) increase for a PV system. This relationship provides
the opportunity to charge a price premium for a high-efficiency module, and provides a
significant barrier to entry for low-efficiency PV technologies. 31
Incumbent
mc-Si
_____
Installed
$1/W
as2eline BOS
E1.0
$0.5/W
Baseline BOS
U)
0
E ~Module
E .Only
L3
0.1
5
10
15
20
Module Efficiency [%]
25
Figure 1.4: Impact of Module Efficiency on Module and System Prices
Increasing module efficiency reduces system prices.'4, 23 While efficiency
provides a significant opportunity, it also represents a significant hurtle for lowefficiency technologies. 3,
20
1.4.3 Enabling Local Manufacturing
Technical cost models were also employed to investigate the prospects of cost-competitive
local manufacturing of PV modules. Recently, the scale of PV manufacturing in China has
accelerated drastically and has coincided with significant decreases in the market share of U.S.based manufacturers.14 In a collaborative effort between the National Renewable Energy Lab
(NREL) and MIT, these trends were studied by taking the perspective of a multi-national
company selecting a new manufacturing location in the U.S. or China. 14 It was determined that
historical MSP advantages in China were not because of inherent factors such as labor, but
rather, scale and supply chain development. Therefore, the MSP of PV manufacturing from both
locations could be equalized. 14 Furthermore, innovation is a potential key ingredient to provide
minimum sustainable price parity by enabling large-scale subsidy-free deployment to erase the
scale difference that currently persists between locations.1 4 Figure 1.5 shows the historical (lH
2012) disparity of MSP between the manufacturing locations and the impact that targeted
advance technology and scale-up have on reaching near-parity MSP.1 4 Indeed, the industry
climate for c-Si PV is challenging, but a focus on long-term scenarios provides technological
guidance and commercial enthusiasm.
21
Innovation Enables Price Parity
1.50
-
1.19
1.25
advanced
0.91
... advanced
technolog
technology...
1.00-
0.64
075-
0.62
... atsc l
.7
0.50
0.25
1H 2012
U.S.
* Module Margin
* Module Costs
* Cash Costs
Adv.Tech Adv. Tech
mCel Margin
ECell Costs
.
China
1H 2012
*Wafer Margin
m Wafer Costs
Figure 1.5: Innovation Enables Price Parity
A combination of technological innovations and scale provides significantly
reduced prices of PV modules and achieves near parity of prices for modules
manufactured in either the U.S. or China. Figure reprinted from.14
1.5 Material Requirements for High-Efficiency Cells
Increasing module efficiency provides the strongest influence on module prices. 2 9 , 32 Highelectrical quality substrate materials are required to support high-efficiency PV cells, and are
therefore of interested for cost-effective PV material development. Following Coletti,
6
Figure
1.6 shows the impact of substrate electrical quality, measured as the carrier lifetime of the
material (detailed in section 3.1), on cell efficiency. The figure was created with a PC 1 D37
simulation for standard and advanced cell architectures described in reference.34 Advanced cell
22
architectures require a higher electrical lifetime to reach maximum performance than a standard
cell architecture.
24
Advanced Cell
22
Cell
20Standard
E
-
0
Z18
16
1
100
10
Bulk Defect Lifetime [ps]
1,000
Figure 1.6: Lifetime Requirements of High-Efficiency Cells
The electrical quality requirements of a substrate material increases with cell
efficiency. High bulk lifetimes >1,000 ps are required for maximum output with
an advanced high-efficiency cell architecture. Simulation inputs from.34
1.5.1 Impact of Miniscule Defect Concentrations in Determining Lifetime
Extremely minute concentrations of defects provide the capability to significantly reduce
lifetime in silicon. 38 In the case of iron contamination in p-type wafers, an interstitial defect
concentration of 1010 atom/cm 3 is required for a lifetime of approximately 1,000 pts, an adequate
level for maximum performance in Figure 1.6.38'39 If 1% of the iron is found as interstitials, then
the 1010 atom/cm3 interstitial concentration allows a maximum total iron concentration of 1012
atom/cm3. While 1012 atoms/cm 3 appears at first glance to be a large concentration of allowable
contamination, it represents a relative concentration of 1 iron atom per 5 x 1010 atoms contained in
the silicon lattice, almost a one part-per-trillion concentration. The regulatory limits on drinking
23
water in the U.S. are quite lax in comparison. Arsenic is limited to a concentration of 1 part per
10 8 and cadmium is limited to 1 part per 2x 108.40 These limits are over 250 times higher than the
allowable levels for iron in silicon. Our bodies are more tolerant to these toxic chemicals, than
silicon is to impurity contamination.
In conclusion, targeting areas for innovation using the framework presented above provides a
mechanism to increase the impact of research and development efforts. It is found that power
conversion efficiency provides the strongest influence on module prices, but requires highelectrical quality substrate wafers. Extremely small concentrations of defects are capable of
reducing electrical quality of substrate wafers to unacceptable levels.
1.6 Thesis Overview
The identification and mitigation of defects that limit the electrical performance in kerfless
crystalline silicon for solar cells is the topic of this thesis. In Chapter 2, historical kerfless wafer
growth methods are reviewed, and epitaxially grown kerfless silicon is introduced. In Chapter 3,
a review of lifetime-limiting defects in c-Si solar cell material is presented. After the review of
lifetime-limiting defects, lifetime measurements are detailed in Chapter 4. Chapter 5 and 6 detail
the ALD A1 2 03 surface passivation employed for lifetime measurements, and phosphorus
diffusion gettering respectively. In Chapter 7, the as-grown performance limits ofp-type material
are presented, followed by the gettering response of p-type material in Chapter 8. Both the asgrown performance and gettering response of n-type material is presented in Chapter 9. Lastly,
the thesis concludes with generalizable conclusions in Chapter 10.
24
CHAPTER
2
EPITAXIALLY GROWN
KERFLESS SILICON
2.1 Standard c-Si PV Module Manufacturing
The standard manufacturing process for c-Si PV includes distinct stages for wafer, cell, and
module production. Wafers are fabricated in the dominant manufacturing process by crystallizing
large ingots (up to 1000 kg) and subsequently sawing the ingots into individual wafers that are
commonly 156x156 mm
with a thickness of 180 pm and p-type doping (boron).4 1'
42
Approximately 50% of the ingot is wasted during this process because of unfit material
(impurities or geometry) and sawdust (kerf) from cutting.4 3 Standard cells are then fabricated
from the wafers with the following process."' 4 Wafers are first textured to improve light
absorption, and then a n+ region is diffused using a phosphorous dopant to form the p-n junction.
An antireflective coating is then deposited on the cell to reduce optical reflection and reduce the
electrical activity of the surface. Metal contacts consisting of fine silver fingers on the sun facing
side of the cell, and aluminum over the full area of the rear of the cell, are then printed and fired
to provide electrical contact. After fabrication is completed, the cells are sorted into groups with
similar electrical behavior. Modules are constructed with the following process.4 3 First, groups of
cells are soldered together with flat wire (tabs) and laminated between two sheets of encapsulant
to provide isolation for moisture. The encapsulant-cell laminate is placed between glass and a
25
backsheet, and then surrounded with an aluminum frame for form a PV module. Lastly, a
junction box with quick-connect wiring and electrical protection is placed on the rear of the
module.
2.2 Kerfless Silicon Technologies
Given the substantial silicon loss of approximately 50% from traditional ingot crystallization
methods due to cropping and wafer sawing loss (kerf),43 and the significant sensitivity of module
prices to silicon feedstock costs (Figure 1.3),29,
32
many alternative technologies have been
pursued for silicon wafer fabrication. 29 An excellent review of these technologies was completed
by Ciszek, 46 with a more recent review by Hahn et al.47 These technologies can broadly be
classified as "kerfless" silicon wafer production techniques, as the cutting loss from traditional
wafer sawing is avoided. Early techniques focused on the vertical pulling of wafers from a
silicon melt, shown in (Figure 2.1, a), and included: dendritic web (single crystal), edge-defined
film-fed growth (EFG), and then String Ribbon.2 9'
46, 48
However, increased rates of growth
(m2 /hr) can be achieved with horizontally grown ribbons (Figure 2.1, b), as a larger surface area
46
is available on the side, rather than end, of a wafer for heat transfer during solidification. '
47
Techniques with this solidification geometry include ribbon growth on substrate (RGS) 47 and the
recently developed direct wafer by 1366 Technologies. 49
Other techniques allow for the kerfless exfoliation of wafers from larger substrates to replace
wire-sawing (Figure 2.1, c). 29 In some techniques a region of subsurface damage is created
through ion implantation to enable the exfoliation of thin wafers without kerf loss.5 0 Other
techniques rely on mechanical stress through the thermal mismatch between the substrate and a
deposited layer for exfoliation.
26
a) Vertical Growth from Melt
b) Horizontal Growth from Melt
c) Exfoliation
d) Epitaxial Growth
y
_____________________
*
1) Damage Release Layer
*Silicon
Gaseous
Source
Suc
Substrate
2) Exfoliate
Substrate
Figure 2.1: Kerfless Wafer Methods
Multiple techniques have been pursued to grow silicon crystals directly in the
shape of a wafer to avoid the cutting loss of wire sawing. Vertical growth from
the melt (a) proceeded horizontal growth from the melt (b). Exfoliation techniques
(c), while not a kerfless growth technology, avoid sawing during the separation of
a larger silicon substrate into thinner wafers. In epitaxial growth (d), the silicon
melt is avoided and the wafer is grown directly from a gaseous silicon source.
An alternative approach of growing wafers directly from a gaseous silicon source via epitaxy
avoids the silicon melt (Figure 2.1, d).5 2 Free-standing epitaxial wafers can be produced with a
layer transfer process (LTP) on a reusable substrate, which is the only component of the process
27
that requires polysilicon production.
52
Epitaxial wafers for solar cells are produced at a lower
throughput of -0.1 m 2 /hr for one 156x156 mm 2 substrate in comparison to -1 m 2 /hr for one 156
mm wide vertical ribbon.46 ' 53 However, advantages including the ability to tune doping
4
55
throughout the thickness of the wafer5 4, and a low average dislocation density <10 cm-2
54 , 56 ,57
The low dislocation density is a significant advantage for these materials, as the structural
defects and impurities contained in other technologies impair performance.58 Record efficiencies
for EFG and String Ribbon materials are 18.2% and 17.8% respectively. 59 Although fabricated
with a different cell architecture than those used on kerfless silicon in the past, the currently
published record cell efficiency for epitaxial silicon is 20.6%.60 Given that module efficiency is
the strongest determinant of manufacturing cost (Figure 1.3), a performance advantage can
provide a significant monetary incentive.
2.3 Kerfless Epitaxy Manufacturing
Epitaxial growth on a reusable substrate with a porous silicon release layer was first
demonstrated with the Canon ELTRAN (epitaxial layer transfer) process. 52 ,61 The process begins
with anodic etching of highly-doped substrate wafer in an aqueous hydrofluoric acid solution to
form a porous silicon layer with a depth of 1 - 2 pm (Figure 2.2, a and b).5 ' '
"
The porous
layer is then annealed at a high-temperature (> 1,000 *C) in a hydrogen atmosphere to close the
pore structure at the substrate surface and form a <100> oriented single-crystal crust that is
approximately 20 nm thick and serves as a template future deposition (Figure 2.2, C).52,
62, 63
The
surface of the substrate provides a sink for vacancies, which dissolves vacancies near the sample
surface and forms a single-crystal pore-free region. 6 2 The uniformity of the single-crystal crust is
an important factor in determining the structural quality, defined as the density of stacking faults,
28
of the resulting deposition.56 The surface roughness of the resulting crust at two length scales is a
strong function of annealing duration. 64 Large-scale micrometric roughness increases with
annealing duration, while small-scale nanometric roughness is observed to decrease.64 The
micrometric roughness has a significant impact on cell performance, thus shorter annealing times
are suggested.64 Furthermore, the thickness of porous region also impacts the structural defect
density, with thinner regions preferred.57 The pore size distribution begins with a lognormal
shape but progresses to a bimodal distribution as large pores within the porous bulk grow at the
expense of smaller pores. 6 2
a) Starting Substrate Wafer
b) Porous Silicon Etching
Substrate Wafer
Substrate Wafer
Porous
Release Layer
Epitaxial
c) Porous Silicon Annealing
d) Epitaxial Growth
Single-Crystal
-Crust
Substrate Wafer
Substrate Wafer
Porous
Release Layer
e) Exfoliation
Growth
Single-Crystal
Crust
Porous
Release Layer
Epitaxial
Wafer
Reused
Figure 2.2: Schematic of Epitaxial Wafer Preparation
A porous silicon layer formed in a single-crystal substrate wafer (a) with anodic
etching (b). High-temperature annealing in a hydrogen atmosphere is then used to
form a single-crystal crust (c) that serves as a template for epitaxial growth (d).
The lamination is then exfoliated (e) providing an epitaxial wafer and reusable
substrate that can begin the process again after surface preparation.
29
High-temperature (> 1,100 *C) chemical vapor deposition (CVD) is then used to deposit the
silicon layer with an average growth rate over 4 pm/min (Figure 2.2, d).12, " Trichlorosilane
(SiHCl 3) is used for the silicon source and reacted with a doping gas, BH 2 in the case of p-type
material and PH 3 in the case of n-type material.
The doping of the wafer can be changed
throughout the growth process, creating layers of different doping types. 55 Impurities within the
substrate are discouraged from diffusing into the growing wafer because of the high doping of
the substrate wafer, with a higher solubility for impurities. 65 Additionally, the porous layer acts
as an effective diffusion barrier.66 Increased growth temperatures are observed to increase
growth rates while also reducing the density of stacking faults in the resulting epitaxy.5 2
After growth, the wafers can proceed to front-side processing while still attached to the
substrate wafer. The wafers are then mechanically exfoliated from the substrate, allowing the
substrate to be reused after subsequent preparation for additional growth cycles (Figure 2.2, e). A
resulting single-crystal epitaxial wafer is shown in Figure 2.3. Over 50 reuse cycles have been
demonstrated with this technology. 53
Figure 2.3: Epitaxial Sample after Exfoliation
The resulting epitaxially grown wafers are single-crystalline and flexible when
deposited at thicknesses < 100 pm. Photo credit. 67
30
CHAPTER
3
LIFETIME-LIMITING
DEFECTS
3.1 Intrinsic Lifetime Limits
As shown in Figure 1.6, a key figure of merit for high-efficiency solar cell materials is a high
bulk carrier lifetime. The bulk lifetime is limited by both unavoidable mechanisms and
unintentional defects and is defined in equation (3.1) below. 6 8 The performance impact of
defects, Tdefects, may be mitigated through engineering. However, the bulk lifetime of the doped
silicon material is limited to a maximum value by intrinsic material limits, Tintrisic, due to Auger
and radiative recombination. 69 Depending on the doping of the material and the cell architecture,
these intrinsic lifetime limits could be a limiting factor of solar cell performance. Figure 3.1
shows the intrinsic lifetime limits as a function of doping and injection level for both p- and ntype silicon based on the model of Richter et al.69 With improved solar cell efficiency and
reduced wafer thicknesses, injection levels of current cells are expected to increase in the future,
potentially above 1015 cm- 34, 70 At this injection level, the intrinsic lifetime of a material is
observed to fall with increased doping concentration (Figure 3.1).
1
Tbulk
_
1
rintrinsic
1
(3.1)
'defects
31
100.00
p-type doping
-5x10 15 cm- 3
--
-
-
1X0
16
3
cm-
5XI016 cm-
n-type doping
10.00
-__----1401s
-5X115
3
cm3
cm- 3
1-0I15 cm54016 cm-
J4
-J
3
3
1.00
-
0.10
Increasing doping limits
maximum lifetime
0.01
1014
1015
1016
Injection Level [cm-3]
1017
Figure 3.1: Intrinsic Lifetime Limits
The intrinsic lifetime limit of a material is unavoidable and reduces with increased
doping concentration and injection level.69 ''
3.2 Lifetime Limits from Avoidable Defects
In order to maximize
Tbulk,
the lifetime impact of defects must be minimized. Equation (3.1)
defines the combined impact of individual defects, such as contamination from transition metal
impurities or structural defects. The defect lifetime is a harmonic sum of all potential terms (3.2).
1
'Zdefects
_
II
Tdeject]
1
Tdefect2
'defect
3
1
(3.2)
Tdefecti
3.2.1 Shockley-Read-Hall Recombination Model
The recombination of specific defects can be assessed with the Shockley-Read-Hal 72' 7
model for non-interacting defects under the assumption that no trapping occurs (An = Ap).52,74
32
The capture rates for electrons, rno, and holes, rpo, [s] of a particular defect are described by
equations (3.3) and (3.4) respectively.52 N is the density of the defect (trap) states [cm- 3 ],
Vth
is
the thermal velocity of electrons and holes [cm/s] defined in equation (3.4), and an and up are the
capture cross-sections for electrons and holes respectively for the trap state [cm 2 . 3 8,
52
T is
temperature [K].
Po =
(3.3)
1
no =
""Nth
an
___I__a(3.4)
p
tN th P
/
vth
T
I. IX 107
\0.5
)05(3.5)
J300
The injection level dependent lifetime of a particular defect is then provided by equation (3.6)
as a function of the capture rates. 38 Here, An is the excess carrier concentration, or injection level
[cm-3], no is the equilibrium electron concentration [cm-3], and po is the equilibrium hole
concentration [cm- 3]. The factors n, and pi are calculated using equations (3.7) and (3.8)
respectively, and depend on the energy levels [eV] of the conduction band Ec, the valence band
Ev, and the trap Et, as well as the effective density of states [cm- 3] in the conduction, Nc, and
valence, Ny, bands defined in equations (3.9) and (3.10). 3 8, 52 Lastly in equations (3.7) and (3.8),
k is the Boltzmann constant.
_rdefect
'(no +n1 +An)+ Iro (p +p, +An)
-p
nd + p+
ni =N exp E -E
p =NV exp
VkTEt
kT
(3.6)
An
(3.7)
(3.8)
33
NC = 2.86 x101 9
X10T 5
N(=3.810
T
300
y.58
(39)
(3.10)
300
3.2.2 Impurity Point Defects
The lifetime impact and tolerable contamination levels of specific defect types can be
assessed with the SRH recombination model in Equation (3.6) using measured parameters of
defect capture cross-sections and energy levels. QSS-Model, created by A. Cuevas, is a useful
tool to complete these calculations.38 Figure 3.2 shows the lifetime impact of impurity point
defects in p-type (a, left) and n-type (b, right) silicon for silicon at a fixed injection level of An =
1015 cm-3.38 Very small threshold concentrations (<1010 cm~ 3) of most impurity point defects are
required to enable the ms lifetimes required for maximum solar cell efficiency (Figure 1.6). In
general, the impact of impurity point defects are less pronounced in n-type silicon than p-type
silicon. The calculations summarized in Figure 3.2
34
a) p-type silicon
10,000
b) n-type silicon
10,000
v 1,000
-1,000
100
.
100
Fe
Ni
1
0Q
3
1
NA=10 6 cm- '
An=1015 CM4
N
-i
O
-Fe
-Fe-B
1ry
-Cr
-Zn
-Cr-B
-V
-Ti
ND=3x1015 m
CM
-CAn=1015
-Co~1ct
1
-- M0
1010 1011 1012
-o
-Fe
-Zn
-Cr
-V
-Ti
-.-
0.1
109
-i
1013 1014
Defect Concentration [cm 4 ]
109
Mo
1010 101
1012
1013 1014
Defect Concentration [cm1]
Figure 3.2: SRH Point Defect Lifetimes at Fixed Injection Level
Impurity point defect lifetimes from SRH model3 at a constant injection level of
An
=
1015 cm 3 and doping concentrations of NA= 1016 for p-type (a) and ND=
3 x 1015 for n-type (b) silicon. Very small concentrations of point defects on the
order of 1010 cm 3 are required for high-lifetime material. The lifetime impact of
impurity point defects is generally lower in n-type silicon than p-type silicon.
While Figure 3.2 provides a useful comparison of point defects, within a solar cell, the
injection level is dependent on the lifetime of the material following Equation (3.11). Here Gav is
the average uniform generation rate throughout the thickness of the wafer, and is dependent on
illumination intensity. QSS-Model was used to simulate the impact of impurity point defects on
bulk lifetime in a solar cell at the maximum power point (MPP) under 1 Sun illumination. 38 The
bulk lifetime of the material, Tbulk, is taken as the harmonic sum (Equation (3.1)) of TSRH for each
impurity type, and the intrinsic lifetime of the material provided by the simplified formulation in
Equations (3.12) and (3.12) for p-type and n-type silicon respectively. 77 A cell thickness of 100
pm was employed with a low saturation current density of J=10- 5 (A/cm 2). Following this
formulation, Figure 3.3 shows the lifetime impact of impurity point defects in p-type (a, top) and
35
n-type (b, bottom) silicon for a solar cell at 1 sun at the maximum power point. A non-linear
relationship between bulk lifetime and defect concentration is observed as low bulk lifetimes
also decrease injection level. At low defect concentrations, the bulk lifetime is limited by
intrinsic limits at relatively high injection, An ~ 1.6x1015 cm-3 at the MPP. At high defect
concentrations, the SRH lifetime dominates. Calculations at the MPP also provide an estimate of
cell efficiency as a function of point-defect concentration (Figure 3.4).38 While some impurity
species are more detrimental than others, a maximum point-defect concentration of 1010 cm-3 is
advised for maximum performance. These curves provide a similar behavior to experimentally
determined impurity sensitivities by the work of Davis et al.75
An
(3.11)
=GA V Ibulk
= (An + NA )(6 x10 -25NA0.6 + 3 x10 -27 An0 8 + 9.5 X10-15)
(3.12)
'intrinsic
1
'rintrinsic
36
=
(An + N, )(1.8 x10-24ND065
+ 3 x10-2 An0 8 + 9.5 X10-15)
(3.13)
a) p-type silicon
10,000
''1,000
EE
100
NA=lxlOOcm
MPP
16Sun,
***10_
10
Im
1
0.1
-Co
-Cr
-CrB
-MO
-Ti
-Zn
-FeB
-Ni
-V
-W
109
1010
Ni
Cr
1011
1012
Defect Concentration
b) n-type silicon
10,000
1014
1013
rcm-31
v
-. 1,000
4)
E
100
ND3x 1015 CM1 Sun, MPP
10
1
Fe
-Co
-Au
-Cr
-Mo
-Ni
-Ti
-Zn
109
Fe
r
-V
-W
1010
0
1ol
1012
1013
Defect Concentration [cm-3]
1014
Figure 3.3: SRH and Intrinsic Lifetime at 1 Sun at the Maximum Power Point
Bulk lifetime from SRH point defect model 38 and intrinsic limits 77 at 1 sun
illumination for a 100 im solar cell with excellent surface passivation (Jo=10~ 5
A/cm2 ). Doping concentrations are NA= 1016 for p-type (a) and ND = 3 x 1015 for ntype (b) silicon. At low defect concentrations, the bulk lifetime is limited by
intrinsic limits. At high defect concentrations, the SRH lifetime dominates. The
lifetime impact of impurity point defects is generally lower in n-type silicon than
p-type silicon.
37
a) p-type silicon
25
Ni
b)
20
10160120
A
1 Sun, MPP
Fe
15
-Co
-CrB
-Mo
-Ti
-Zn
1 0 n10,
W
-Au
e
-FeB
-Ni
-V
-W
Cr
11
M
Fe -Au
-Co
- Cr
N1=3x1010
-Mo
-Ni
-15
Z
-Ti
-Zn
10
TI
-Crr
-Vn
-W
lol
102
I
113
11
o
_j
Defect Concentration [cm-3]
Figure 3.4: Simulated Cell Efficiency and Point-Defect Concentration
SRH point-defects reduce simulated cell efficiency3 at 1 sun illumination for a
100 p~m solar cell with excellent surface passivation (J=10-15 A/cm 2 ) . Doping
concentrations are
NA
=
1016
for p-type (a) and ND =3 x 1015 for n-type (b) silicon.
The efficiency impact of impurity point defects is generally lower in n-type
silicon than p-type silicon. Tolerable point-defect concentrations to avoid
.
performance loss are on the order of 1010 cm-3
3.2.3 Structural Defects
Structural defects such as grain boundaries and dislocations also provide a potential limit on
78
bulk lifetime. Dislocations are recombination active if decorated by impurity species. ,
79
Furthermore, dislocations effectively trap impurity species and make them difficult to remove
38
during processing. 80' 8 The lifetime impact of dislocations are dependent on the baseline lifetime
of the material in non-defective regions and the recombination strength of the dislocation.8 2
Figure 3.5 shows the lifetime impact of structural defects following the model by Donolato, 82
with parameters from Choi et al.8 3 Material with a higher baseline lifetime in defect-free regions
is more sensitive to dislocations. Increased recombination strength of dislocations is also
observed to increase lifetime impact. A simpler correlation shows a reduction in the performance
of a solar cell with increased area-coverage of dislocated regions. 84 Because of the inherently
low structural defect densities in epitaxially grown material (Section 2.2), structural defects are
expected to play a relatively small role in recombination within epitaxial material.
39
1
1,000
'
.
E
Increasing
s- --...
'
recombination strength
increases impact of
dislocaitons
Material with a higher
.baseline lifetime is
degraded at a lower defect
density
100
,=100
ps
10
102
103
104
105
106
Defect Density [cm-I
Figure 3.5: Lifetime Impact of Structural Defects
Following the Donolato model8 2 with input from Choi et al,83 structural defects
reduce lifetime. Material with a higher baseline lifetime is more sensitive to the
presence of dislocations. Dislocations with a higher recombination activity are
more potent at reducing lifetime.
40
CHAPTER
4
LIFETIME MEASUREMENTS
4.1 Surface Lifetime Limits
The bulk lifetime of the substrate wafer determines ultimate device efficiency as shown in
Figure 1.6, but is not directly measured. The effective, teff (measured), carrier lifetime is a
function of both the bulk lifetime, Tbull, and the surface limited lifetime of the wafer, Tsurface in
Equation (3.1).68 The value of reff is not necessarily dependent on the quality of the bulk material
under study, Tbulk, but rather on the effectiveness of surface passivation, rsufface. The effectiveness
of surface passivation is computed with the surface recombination velocity (SRV). 85 A favorable
SRV is low, which results in a high
lsurface
and makes Teff and Tbulk approximately equal in
equation (4.1). The impact of SRV on the equivalence of rTe and Tbulk in p-type silicon (NA
=
1016
cm 3 , thickness = 100 pm) is shown in (Figure 4.1).85 For thin high rbulk materials, good surface
passivation (<10 cm/s) is required to measure a high reff and to be sensitive to changes in Tbul.
Additionally, a high rintfinsic material is required to measure a high Teff and to be sensitive to
changes in the lifetime due to defects in the material.
41
1,000
800
75400
SRV [cm/s]
20 0
/-3
-5
W
------ =100 Pm
D =30.27 CM2/s
-----5 10
-j- 20
0
0
200
400
600
800
1,000
Effective Lifetime [psi
Figure 4.1: Bulk Lifetime vs. Measured Effective Lifetime
The effectiveness of surface recombination is characterized as the surface
recombination velocity. Effective surface passivation is required for the
measurement of high effective lifetimes on thin, high bulk-lifetime materials. A
low surface recombination velocity increases the sensitivity of measurements to
bulk lifetime.
1
Teff
(4.1)
1
1
Tbulk
surface
4.2 Injection Level Dependent Lifetime Measurements
The effective lifetime, teff, of a material is measured with a Sinton WCT-120 tool as a
function of injection level.
8
The measurement subjects a sample to a brief pulse from an
exponentially decaying flash lamp, and then measures electrical activity in the wafer. Multiple
42
measurement modes including Quasi Steady State Photo Conductance (QSSPC) and transient
photo conductance decay mode are available. In QSS mode, the wafer response is assessed while
under quasi steady-state illuminated by a slowly-decaying flash. In transient mode, the wafer
response is assessed after a rapidly-decaying illumination source has decayed. A third
measurement mode, generalized, combines the lifetime measured through both the QSS and
transient modes and is given by equation (4.2) from Ref.68 The effective lifetime, reff is given as a
function the injection level, An, generation rate, G, and the time derivative of the injection level.
In QSS mode, the time derivative of the injection level is set to zero, dAn/dt = 0, while in
transient mode, the generation rate is set to zero, G = 0.
1
An
I
An
reff G -dAn
(4.2)
dt
Examples of injection level dependent measurements from the Sinton WCT-120 are shown in
Figure 4.2. The injection level dependence of the data is significant, as the behavior of multiple
defect types varies with injection level.
4.2.1 Measurement Noise
Data from measurements on samples with a high lifetime often includes significant noise. A
small amount of noise in the input photovoltage signal propagates through Equation (4.2).68 The
averaging of 50 measurements is observed to satisfactorily reduce noise in the calculated
material lifetime (Figure 4.2), though even 10 measurements provides a noticeable reduction in
noise. With some samples however, it has been observed that illumination from the Sinton-WCT
120 flash lamp impacts the measured lifetime. The observation of lifetime improvement with
flashing using float zone substrates suggest that this phenomena is due to instability in the A1 2 03
43
passivating layer itself. While this phenomenon is not completely understood, it is suggested that
only the necessary number of measurements are employed for adequate averaging of the final
result.
1900
Averaged Measurements
1800
5
1
10
*20
50
-- 90
1700
E
Z 1600
1500
1400
1014
1015
Injection Level [cm-9
Figure 4.2: Reduction of Measurement Noise with Averaging
Averaging multiple measurements effectively reduces noise in a Sinton WCT- 120
measurement. Because of suspected interactions with the passivating layer
however, utilizing only the minimum number of measurements required to reduce
noise is suggested.
Modifying the default time derivative routine within the Sinton WCT-120 software was also
observed to effectively reduce measurement noise (Figure 4.3). The default Sinton WCT-120
settings dAn/dt calculation utilizes five points (2 leading, 2 trailing) using a linear regression
with the included data points. Increasing the data range too nine points provided a significant
reduction in noise, while decreasing the number of points to three provided an increase in noise
(Figure 4.3). Other alternative "Noise-robust" differentiators from ref86 did not provide an
44
effective increase in performance, though it is unclear if the linear differentiation method used by
the Sinton WCT-120 excessively dampens the result in comparison to more sophisticated
methods.
1800
1700
= 1600
A
0
Average 50, N=3 Linear Regression
15wJ
Average 50, N=5 Linear Regression
50, N=7 Linear Regression
+ Average 50, N=9 Linear Regression
&
a Average
1400
1014
1015
3
injection Level [cm- J
Figure 4.3: Number of Points for Time Derivative Noise Comparison
Increasing the number of points considered for the time derivative from the
default number of 5 effectively reduces noise in the lifetime data.
4.2.2 Impact of Room Illumination
Room illumination was investigated as a potential noise source in the Sinton WCT-120
system. While turning lights off in the laboratory for measurements were not observed to impact
noise, a bias in lifetime results at low injection levels was exposed (Figure 4.4). With samples
exposed to room illumination, a bias towards higher injection levels is introduced under room
lighting. This bias is significant enough to improve lifetime by approximately 100 ps in high
lifetime material (Figure 4.4). It is suggested that lifetime measurements should be conducted
when samples are not exposed to room illumination.
45
1800
1700
A
1600
A&
A
s
A
1500
A Average 50
N Average 50 - Lights Off
1400
1014
1015
Injection Level [cm4]
Figure 4.4: Impact of Room Lighting on Effective Lifetime
Room lighting was not observed to increase noise in the measurement. However,
a pronounced impact on low injection level lifetime is observed, as the room
lighting has increased the background injection level of the material.
4.2.3 Impact of Flash Lamp Head Height
The impact of changing the head height of the tool was also investigated with the highlifetime A1 2 0 3 passivated float zone sample and an as-diffused single crystal silicon sample.
Fifty measurements were averaged for the float zone sample with transient analysis and a 1/64
flash. Generalized analysis with a slowly-decaying 1/1 flash was used for the as-diffused sample
with averaging of three measurements. In neither case did the head height provide a significant
variation in measured lifetime. Similarly, the inclusion of the IR band pass filter was not
observed to significantly impact measured lifetime with these samples. However, in the case of a
sample with a more severe surface limited lifetime, this trend may not be observed.
46
4.2.4 Minimum Sample Thickness
In order to maintain the assumptions of uniform carrier density in the QSSPC measurement, a
minimum sample thickness of twice the diffusion length of the material is enforced during
measurements. 68 '
87, 88
This condition arises with low lifetime material. Because the calculated
lifetime is a function of the sample thickness, a solver was implemented to converge on a
calculated lifetime and sample thickness that satisfies this constraint. The diffusion length was
calculated following equation (4.3) below, with LD as the diffusion length in cm, D as the
minority carrier diffusivity in cm2/s and r as the carrier lifetime in seconds.68 The minority
carrier diffusivity is a function of doping for both p-type and n-type materials.8 9
LD
=-,D
(4.3)
4.2.5 Reference Cell Aperture
In addition to the diffusion length limitation, low lifetime material may require additional
illumination to reach an injection level of 10" cm- 3.68 This can be accomplished by lowering the
flash lamp to a height of < 200mm above the sample. At this flash lamp height, the reference cell
signal will violate its saturation limit of 0.6 Volts. The reference cell signal however, can be
attenuated with an aperture supplied by Sinton Instruments following the procedure in Table 4.1.
47
Table 4.1: Reference Cell Aperture Procedure
Step
Procedure
1
Acquire data with a moderate lifetime sample with a low sample head height
without an aperture over the reference cell. Note the lifetime at an injection
level that is not impacted by the saturation of the reference cell.
2
Tape the aperture plate over the reference cell. The 2nd largest aperture has
proven appropriate for low flash lamp measurements in the past.
3
Acquire data again. The reference cell data acquisition ranges will likely need
to be zoomed.
4
Use the goal seek function to modify the reference cell value [V/sun] until the
lifetime before and after the aperture match. Note the revised reference cell
value for future measurements in this condition. However, begin new
measurements without the aperture with a different baseline spreadsheet to
preserve the original reference cell value.
5
Lower the flash lamp as needed to reach the injection level of interest. The
diffusion length solver discussed above should be used for an accurate
calculation of injection level with low-lifetime samples.
4.2.6 Optical Constant
In QSS and Generalized measurement modes, the lifetime calculation requires an optical
constant to factor in the amount of incident light from the flash lamp that is absorbed by the
sample. 68 A PC1D1 7 simulation, provided by Sinton Instruments, was utilized to solve for the
optical constant for both bare samples and those passivated by aluminum oxide with and without
surface texture (Figure 4.5). The refractive index and extinction aluminum oxide were provided
9 1 92
by ref,90 with the reflection calculated by QSS-Model. ,
48
0.75
4W
0.65
--
-
0
C-
Bare - Textured
-*-Bare - Flat
-+-20nm A1203 - Flat
-.- 20nm Al203 - Textured
.-
0.55
1
50
100
150
200
Wafer Thickness [prm]
Figure 4.5: Optical Constant for QSS and Generalized Measurements
Optical constant calculated with PCiD3 simulation.68 The amount of absorbed
illumination is reduced with reducing sample thickness.
4.2.7 WCT-120 Lifetime Comparison
The Sinton WCT-120 tool in the MIT PV Lab was used to measure a standard set of wafers
used in a round-robin study of Sinton WCT-120 lifetime testers deployed throughout the world,
though not included in the linked reference. 93' 94 The tool used within this thesis fell within the
distribution of the deployed systems
4.3 p-PCD Lifetime Mapping
Lifetime measurements provided by the QSSPC tool provides information about the variation
of lifetime with injection level. This is especially important for identifying the nature and
concentration of recombination active defects such as iron (Section: 4.4). However, spatial data
indicating the variation of lifetime over the surface of a sample is not provided with this
measurement method. Microwave-induced photoconductive decay (pt-PCD, Semilab WT-2000)
49
is used to provide spatial lifetime measurements over the surface of the sample. A 904 nm laser,
which corresponds to a absorption depth of approximately 30 pm in silicon, is used to excite
carriers in the sample.95 A detector is used to sense the microwave reflectivity which depends on
the conductivity of sample.95 The transient decay of the conductivity signal after a 200 ns laser
pulse is used to calculate the lifetime. The excitation area of the laser is approximately 1 mm2
However, the X-Y stage that supports the sample can be moved with a resolution as low as 62.5
pm. Figure 4.6 shows lifetime maps for a float zone silicon sample passivated with A1 2 0 3 with a
resolution of a) 62.5 pm and b) 1 mm. Although the method oversamples at resolutions less than
1 mm, useful definition is observed in the maps.
The injection level of the measurement has been investigated with this measurement
technique without a bias lamp. 96,
97
However, the actual injection level and lifetime of the
measurement still contains significant uncertainty. 98 However, although more qualitative in
nature, p-PCD measurements are valuable for identifying the distribution of recombination
active defects and potential evidence of contamination sources. As explained in (Section: 4.4), pPCD can also be used for the analysis of meta-stable defects such as Fei-B pairs, though the
unknown injection level of the measurements limits its application herein to a qualitative
analysis.
50
a)
b)
10 mm
U 200us
E000
l us
Figure 4.6: p-PCD Map of Float Zone Sample
pt-PCD map of float zone silicon sample with Al 2O 3 passivation with a scanning
resolution of a) 62.5 prm and b) 1 mm from two separate measurements.
4.4 Interstitial Iron Measurement
Iron contamination substantially reduces the lifetime of p-type silicon material 75 ' 11 and is
common in manufacturing environments. Given the large electrical impact of the impurity, low
concentrations of contamination can be accurately measured via lifetime spectroscopy.' 00' 101 Iron
forms metastable defects with boron dopants in p-type silicon that have different levels of
electrical activity than isolated interstitial iron." '0'
In the equilibrium state in p-type silicon,
positively charged interstitial iron pairs with negatively charged substitutional boron forming
Fei-B, pairs. 12 However, these pairs can be dissociated through suitable illumination or hightemperature annealing. 88 Via injection level dependent lifetime measurements with Fei-Bs paired
and dissociated (Figure 4.7), the iron point defect concentration is calculated with equation
(4.4).101 In the equation, [Fei] [atom/cm 3] is the iron point defect concentration, C [ps cm 3 ] is the
51
pre-factor,
TFei
[s] is the carrier lifetime with iron interstitially distributed after the dissociation
of iron-boron pairs, and
TFei-Bs
I
[Fe,] = C
TFei
-
[Ps] is the carrier lifetime with iron paired with boron.'O'
1
(4.4)
Fej -Bs
40
Fe -B, Dissociated
30
.
35
C ...*ro
25
E 20
-J
15
10
Crossover Point
5
0
1013
1014
1015
Injection Level [cm-1
1016
Figure 4.7: Injection Level Dependent Lifetime of Metastable Iron Defect
Iron has different levels of electrical activity in p-type silicon depending on it
pairing with boron dopants. The concentration of interstitial iron can be measured
through lifetime measurements with interstitial iron paired with boron, Fei-B,, and
when pairs are dissociated, Fei.
4.4.1 Pre-factor
The value of the pre-factor varies as a function of the base doping of the wafer, carrier capture
cross-sections, and the injection level of the lifetime measurements in equation (4.4). The prefactor is shown in Figure 4.8 as a function of doping level and injection level with capture cross
sections taken as or = 5.0 x10-15 cm2 and up = 3.0 x10- 5 cm 2 for Fei-Bs pairs and u, = 1.3
52
x10-
cm 2 and p = 7.0 xI0- 17 cm 2 for Fei. 100, 103-105 Defect energy levels are Ec-Et = 0.744 eV for Fei and
Ec-Et = 0.26 eV for Fei-Bs pairs.'00 A crossover point is observed in the injection level dependent
lifetime data of an iron-contaminated sample in Figure 4.7. Above this point, dissociated Fei
provides higher lifetime than Fei-B, pairs.100 Below the crossover point, Fei-Bs pairs provide
higher lifetime than dissociated Fei.1 00 The injection level of the crossover point can be predicted
with a Shockley-Read-Hall (SRH) recombination model.10 2 Fei is a deeper level than Fei-Bs and
has a lower electron (minority carrier in p-type silicon) lifetime coefficient that dominates in
low-injection conditions.38 However, at high injection conditions, the impact of both minority
and majority carrier recombination is pronounced. 106 Fei-Bs pairs have a lower majority carrier
lifetime coefficient than Fei that overcomes the minority carrier advantage of Fei at highinjection conditions. 38'
106
The crossover point is observed in the calculation of the pre-factor
(Figure 4.8).100 Within a narrow injection level regime around the crossover point, the
relationship between the pairing of iron point and lifetime becomes erratic (Figure 4.8). To avoid
this regime, measurements are completed at a minimum injection level of approximately 0.1 x
NA, the boron doping concentration. Error bounds for the [Fei] calculation are determined
assuming a lifetime measurement error of 1.5%93 for repeated measurements of lifetime on a
single Sinton lifetime tester and random propagation through Equation (4.4).107
53
2.xx1014
Doping [1/cm3
2 0
1.QxlO'
-
-
1.5x10'4
-
1.5-
5x1015
16
-1x10
5x1
6
-5l'
4
;71.0 x1013
0
-5.Ox1O13
-1.0x1014
-1.5xJ1'4
-2.0x 1014
1015
1014
1016
Injection Level [1/cm 3]
Figure 4.8: Pre-factor for Iron Calculation
The pre-factor for the iron point defect concentration is a function of wafer
doping and the injection level of the measurement. An inflection point is observed
in the injection level dependence of the pre-factor. 100 To avoid the crossover
point, measurements should be conducted at a minimum injection level of
approximately 0.1 x NA.
4.4.2 Fei-B, Dissociation
Fei-B, pairs are dissociated through adequate illumination or thermal annealing. Previous
studies have used 2 min of illumination at approximately 2 suns (200 mW/cm 2)
87
', 88
5 min of
illumination at approximately 1 sun (100 mW/cm 2 ), 10 1 approximately 20 flashes by a highintensity flash (Hensel EHT 3000 in Semilab WT2000, estimated 500 suns for 10 ms)1 08' 34 or
annealing at > 200
C. 8 8 '
109, 110 The equilibrium concentration of Fei-Bs pairs are given as a
function of temperature, T in K, and doping, NA in cm~ 3 , in Equation (4.5). 101 Figure 4.9, a shows
54
the required temperature to dissociated 99%, 90%, and 70% of Fei-B, pairs as a function of
doping. High temperatures are required to dissociate 99% of Fei-B, pairs.
1023 NAexp 0.65)
(4.[Fe
5) 1 B,]
-
kTU
[ Fei ]
The initial rate of Fei-B, pair dissociation, Fd [/s] under illumination follows Equation
(4.6).111,
112
Gav is the average (uniform) generation rate in cm-3 s-I in the sample and [Fei-Bs] is
the concentration of recombination centers in cm-3. Figure 4.9, b shows the time required for
dissociation at four generation rates, 11 1,112 and the approximate illumination intensities in suns as
estimated with a wafer thickness of 100 pm with 20nm of A1 2 0 3 . 3 8 Heavily contaminated
samples require substantial illumination times > lhr to dissociate Fei-Bs pairs. Because of this
dependence, thermal dissociation is suggested for Fei-Bs pair dissociation of heavily
contaminated samples. However, an initial dissociation of 90% may significantly reduce the
required thermal input.
-'= 5 x 10-"
d
G2
av
(4.6)
[FeiBs]
55
a) 500
b)_103
102
10,
450
400
450
-
102
200
~350
0
~300
- -- 'U
.ol'I
'1"01
.-..
10
10-2
3
1a
1
250
IVefect
0~10-5
4FB
-
200
150
ps
--
s
~
\
-;
..
,
-
E 10-1
10g1
ferom
.10-a
100
1015
1o6
a)
I
fo
e empraur
Boron Doping Concentration [cm-1
101
99%
9%
10"l
1012
1010
109
Fe-B- pair
ands
1eqire 7* discito Sf
Interstital
Iron Concentration [cm-3I
1013
Figure 4.9: Fei-B, Pair Defect Dissociation
a) The temperature required for 99%, 90%, and 70% dissociation of Fei-B, pairs
from annealing. b) The time required for the dissociation (5 time constants) of
Fei-B, pairs from illumination at three generation rates. 10 5'106 For highly
contaminated samples, long illumination times > 1 hr is required for dissociation.
To avoid dissociation, a maximum integrated absorbed energy of 0.01 J/cm 2 is specified.101
The integrated absorbed energy is estimated at 7 x 10-4 J/cm 2 and 0.02 J/cm 2 for a Sinton WCT120 flash lamp in 1/64 and 1/1 modes respectively. Therefore, the 1/1 flash mode is expected to
dissociate Fei-B, pairs. However, experience has shown that the 1/64 flash mode is also capable
of modifying the distribution of iron within a sample, requiring minimum measurements for
lifetime in the associated state.
4.4.3 Fei-B, Association Time
The time required for the association of Fei-B, pairs is a function of the boron doping
concentration and ambient temperature.87 The time constant for association is provided by
Equation (4.7), based on the recently updated work for Tan et al.8 7'
108
The time required for
99.3% association (5 time constants) of Fei-B, pairs for the three doping levels shown in Figure
56
4.8, (5 x10" cm-', I x10" cm~ 3 , and 5x1016 cm 3) are shown in Figure 4.10 as a function of
temperature.87'
3
It is recommended that associated measurements are conducted at the
minimum time required to form Fei-B, pairs to avoid convolution with other metastable defects
that may have a different time constant for association, such as chromium.88
S
=5.7 x10 5
(4.7)
0.66
kT
T exp
N
10
E
0
ol1
0
Doping [1/cm 3]
L-
-
*5x10'5
1x1016
--- 5x1016
0.1
i
20
22
24
26
28
30
Ambient Temperature [DC]
Figure 4.10: Fer-B, Pair Defect Association Time
The time required for the association (5 time constants, 99.3 % formation) of the
'
Fei-Bs pair is a strong function of boron doping concentration and temperature.8 7
113
4.4.4 Measurement Process
The [Fei] measurement process is shown in Figure 4.11. The sample is first flashed 15-20
times with the flash lamp within the Semilab WT2000. Then, within 30 seconds of the final
flash, injection level dependent lifetime measurements are completed with the Sinton WCT-120.
57
In the case of low-lifetime samples, single generalized measurements are completed in
increments of 30 seconds until approximately 150 seconds have elapsed since the final flash. In
the case of high-lifetime samples that require significant averaging of transient measurements to
minimize noise (Section 4.2.1), a single measurement with many averages may be taken. The
sample is then stored in a dark enclosure for an adequate time for Fei-Bs pair association.
Lifetime is then re-measured with the Sinton WCT- 120. Here a single measurement is completed
in low-lifetime samples, and a reduced number of measurements are utilized for high-lifetime
samples, as the Sinton WCT-120 measurement flash lamp has been observed to modify the
sample lifetime, potentially through an interaction with Fei-B, pairs. A p-PCD measurement is
then completed on the associated state sample, followed by another 15-20 flashes to re-dissociate
Fei-Bs pairs and a ji-PCD measurement in the dissociated state.
Illuminate
Illuminate
Flash 15-20 QSSPC
times to
lifetime
dissociate
with Fei
Fe-B
QSSPC
after
association
of Fe-B
pPCD
lifetime
map with
Fe-Bs
paired
Flash 15-20
times to
dissociate
FerBs
pPCD
lifetime map
with Fei
Figure 4.11: Iron Boron Pair Dissociation Experimental Schematic
58
CHAPTER
5
SURFACE PASSIVATION
Bare silicon surfaces provide an effective recombination pathway with a recombination rate
Us, in cm 2 s-1 , following the SRH recombination model defined in Equation (5.1).90' 114 The
recombination rate may be reduced through a reduction in the interface defect density Dit, in cm2, or the concentration of carriers at the surface (n, and ps). 90 The surface recombination velocity
S, in cm/s, is then defined following Equation (5.2),114 and allows the surface limited lifetime to
be calculated following Equation (5.3). This expression assumes both sides of the sample are
identically passivated, which has an error of less than 5% over a large range of surface
recombination velocities.85 ' 115 The expression however is most accurate when the surface
lifetime is dominated by either the effectiveness of passivation at the surface or carrier transport
to the surfaces (SWID << 1 or SW/D >>10)."5
u
=
ns +nl + pS+p0Op
(5.1)
On
(5.2)
S= Us
An,
surfce =W
2S
W
+1
2
(5.3)
D7)
59
In addition to the surface recombination velocity, the sample thickness W in cm, and D the
minority carrier diffusivity in cm 2/s significantly influence surface lifetime limits of a sample.
Therefore the effect of surface recombination becomes critical for thin samples. As discussed in
Section 4.1, excellent surface passivation is required to obtain high effective lifetime and to be
sensitive to changes in bulk lifetime. Figure 5.1 shows the surface limited lifetime as a function
of sample thickness and the surface recombination velocity (SRV) for a silicon wafer. 8 5
Relatively small changes in SRV and sample thickness have a large impact on the resulting
effective lifetimes. A SRV < 10 cm/s is required to measure lifetimes up to 1,000 ps on a 200 pm
thick wafer, while a SRV < 5 cm/s is required for a 100 pm thick wafer.
2,000
SRV [cm/s]
-- 5
--- 10
-- 20
S1,500
- 1,000
/*
0
500-
0
0
50
100
150
Sample Thickness [pm]
200
Figure 5.1: Surface-Limited Lifetime
The lifetime limitation of a silicon wafer surface is defined by wafer thickness
and the surface recombination velocity of the passivating layer. Excellent surface
passivation is required to measure effective lifetimes >1,000 ps with 100 pm thick
wafers.8 5
60
5.1 ALD Aluminum Oxide
Aluminum oxide (A12 0 3) deposited by atomic layer deposition (ALD) was utilized as a
passivation layer for lifetime measurements. Extensive literature is available on A1 20 3
passivation, and is summarized in Ref.90 A1 2 0 3 reduces the electrical activity of sample surfaces
by repelling charges with a field-effect and reducing the density of interface states. 90'
116
The
fixed negative charge in an A1 2 0 3 layer effectively repels the minority carrier electrons in p-type
silicon by forming an accumulation region.90 In n-type silicon, the fixed negative charge forms
an inversion condition near the sample surface. 90 Very good SRV values < 5 cm/s have been
achieved with A12 0 3 .90 The processes utilized are detailed in Appendix D.
A thermal ALD system (Cambridge Nanotech Savannah 200) was used at a temperature of
200 *C with water as a precursor to deposit that passivating layer. An ALD reaction includes the
formation of single monolayers on the wafer surface, and allows for the alternation of species.
This regime however, requires adequate purging after injection of a precursor gas into the
reaction chamber to allow excess precursors vent. Figure 5.2 shows the pressure inside the ALD
reaction chamber after injection of water and the aluminum precursor at a temperature of 200 'C.
A 9 second of purge duration for both water and the aluminum precursor were determined as
adequate for the reaction chamber to obtain roughly base pressure and avoid a less uniform CVD
deposition mode. 90
61
0.75
--- Water
-- Aluminum Precursor
0.70
0.65
0.60
0.55
...
0.50
0
2
4
6
Time [s]
8
10
12
Figure 5.2: Pressure Decay inside ALD Reaction Chamber
A purge time of 9 seconds was employed to allow the chamber to recover to the
base pressure after the injection of material precursors.
The standard operating procedure for passivation is detailed in Appendix D. If samples are
diffused before passivation, the emitters are removed following the procedure in Appendix C.
Samples are first chemically cleaned with RCA process that does not include a final HF dip to
preserve the oxide that results from the RCA2 step. Samples are placed atop chips of single
crystal silicon within the reaction chamber to enable deposition on both sides of the samples.
After roughly positioning the chips to support the wafers with a minimum of sample contact, 5
nm of A1 2 0 3 is deposited in an attempt to isolate the samples of interest from any contamination
that is present in the reaction chamber of the shared-use tool. After the deposition of the capping
layer and sample loading, 20 nm of A1 2 0 3 is deposited on the samples over 170 cycles. The
samples are then annealed at approximately 350 *C in a N 2 atmosphere.
The electrical characteristics of the passivation layer are assessed with a contactless voltagecapacitance (V-Q) measurement technique (Semilab WT2000).95 During the measurement,
62
charge (Q) is deposited on the sample surface while the surface potential (V) is monitored with a
Kelvin Probe.95 The fixed negative charge density (Qtotai) in the film is then assessed as the
amount of deposited charge that provides a surface potential of zero. 95 The density of interface
states (Dit) is then calculated by comparing the actual V-Q measurement curve to an idealized
measurement with Dit = 0.95 An A12 0 3 passivation film deposited and measured using the above
procedures yielded a fixed negative charge density of
Qtotai =
-1.15
x 10
cm-2 and density of
interface defects of Dit = 1.65 x 1011 eV~1cm- 2 . These values fall within the range of the expected
properties of A1 2 0 3 passivation.90
5.2 Measuring Passivation Effectiveness
The effectiveness of the passivating layer can be assessed by comparing the measured
effective lifetime (Teff) of the wafers to the maximum intrinsic lifetime given by the model of
Ricther et al.69 This is completed by assuming the bulk lifetime
(rbulk)
of the material in Equation
(4.1) is equal to the intrinsic limit (rintrinsic). Because of this assumption, the use of high-purity
float zone (FZ) silicon is preferred for this calculation. Equation (5.4) provides the surface
limited lifetime (Csurdace) as a function of these parameters. 85 Additionally, because the doping or
surface roughness of the sample may impact the effectiveness of surface passivation, the control
sample should match the composition of the samples of interest. 90'
117
Following (5.3), the
resulting surface recombination velocity is given by Equation (5.5).
suface
-
eff
S
(5.4)
eff* 'intrinsic
~
intrinsic
(5.5)
DIc 2 W
2Dff2rsurface - 2W
2
63
This calculation can be completed with an injection level dependence. Figure 5.3 shows the
effective and intrinsic lifetimes for a 3 0cm, p-type, 253 nm thick, FZ control wafer after A1 2 0 3
passivation with the calculated surface recombination velocity. As in Figure 3.1, the intrinsic
lifetime falls at high injection levels. The resulting surface recombination velocity, as calculated
by Equation (5.5), is also a function of injection level. However, for the purposes of analysis, a
constant surface recombination velocity is applied from the lower injection level regime. In an
alternative method to that presented herein, the surface recombination velocity can also be
calculated at multiple sample thicknesses. Ongoing work in A1 2 0 3 passivation includes
characterizing the stability of passivated samples.
Intrinsic performance limit
float zone control
-
15
10,000
13
Effective lifetime, float zone control
9
1,000
0.
Calculated surface
recombination velocity
5
100
1014
1015
1016
Injection Level [cm-1
Figure 5.3: Lifetime and SRV of Float Zone Control Wafer
The effective lifetime of a float zone control wafer (blue) is compared to its
intrinsic performance limit (red) to calculate the surface recombination velocity of
the passivating layer (green). The surface recombination velocity is observed to
increase at high injection levels.
64
5.3 Corona Charge of Surface Passivation
Depositing additional negative charge on the sample surface can be used to increase the
effectiveness of the field-effect passivation already provided by the fixed negative charge of the
A1 2 0 3
layer. Corona charge was deposited on samples using a small diameter wire placed over
the surface of the sample at a voltage of 10 kV (Semilab WT-2000). The fixed charge of an
A1 2 0 3 passivation layer is generally on the order of -1 x 10" to -5
x
102 cm-2 (approximately
-160 nC/cm2 to -800 nC/cm 2 ) and was Qtotai -1.15 x 101 cm~2 on a A1 2 0 3 film deposited with
available equipment and procedures. Figure 5.4 shows an improvement in measured effective
lifetime and a reduction in the calculated surface recombination velocity with the deposition of
additional negative corona charge. The resulting surface recombination velocity of < 3 cm/s is
exceptional.1 9 However, it is noted that excessive corona charge can cause damage to the
passivation layer. The range assessed herein did not appear to degrade passivation quality.
900
4.0
850
3.5
800
3.0
E
750
2.5
Effective lifetime
-
.E
Calculated Surface
Recombination Velocity
700
2.0
0
600
1200
1800
2400
3000
3600
Negative Corona Charge [-nClcm2
Figure 5.4: Lifetime Improvement with Corona Charging of n-type Samples
The effective lifetime at an injection level of 1015 cm-3 of a n-type sample
increased and the calculated upper bound surface recombination velocity
decreased with the deposition of corona charge. The SRV upper bound was
calculated assuming a bulk lifetime equal to the Ricther et al limit.69
65
CHAPTER
6
PHOSPHORUS DIFFUSION
GETTERING
6.1 Internal vs. External Gettering
Impurity gettering consists of the transport of harmful impurity species (Figure 3.3) from
detrimental wafer areas to more benign locations. 39 A gettering process can move impurity
species towards a surface for potential removal with an external gettering process, or towards
pre-determined locations within the bulk of the wafer through an internal gettering process.118
Internal gettering processes, such as to intentional silicon-oxide precipitates, 39 are not
traditionally used in PV manufacturing as the entire wafer is electrically active. Phosphorus
diffusion gettering (PDG) is a commonly employed external gettering technique for PV
manufacturing that simultaneously getters impurities and forms the p-n junction for p-type solar
cells. The gettering process consists of three primarily steps. First, an impurity species is
unlocked from its detrimental location through an increase of diffusivity and solubility with
temperature.3 9 The impurity then diffuses to the heavily phosphorus doped n+ region driven by a
difference in solubility and is captured in the heavily doped gettering layer. 39 The relationships
between the driving force for gettering, the diffusivity of impurities and their rate of dissolution
are strong functions of temperature. 39
66
6.2 Phosphorous Diffusion Gettering
The progression of a gettering process is shown schematically in Figure 6.1.119,
120
First, in an
as grown wafer, impurity contamination is distributed between precipitate and interstitial
locations. Then, during high-temperature phosphorus diffusion, the heavily doped n+ emitter
region begins to form and precipitated impurities dissolve. Some interstitial contamination
begins to be gettered to the emitter. During cool-down, the driving force for gettering increases,
as the solubility difference between the emitter and bulk rises, while the diffusivity of interstitial
contamination falls. Also during cool-down, some interstitial contamination forms precipitates
through a relaxation gettering process.3 9
67
Evolution of Metal Impurities
As-Grown Wafer
0
*0
During Cool-down
Emitter
o
.
.
*
During High-T Diffusion
T
~tttn
ulk
s0rPcpecipeta
Precipitate
Lnterstitial
L=
0.
E
Time
Figure 6.1: Evolution of Metal Impurities during Phosphorus Diffusion Gettering
As-grown material contains impurities that are distributed as interstitial defects
and precipitates. During phosphorus diffusion gettering, interstitial impurities
diffuse towards a heavily doped n+ gettering layer while precipitates dissolve.
During cool-down, the driving force for gettering increases while interstitial
defects precipitate.
A standard, high-throughput, gettering process includes a single high-temperature diffusion
step and exponential cooling to room temperature from the plateau step (Figure 6.2, Red). For
some materials,3 4 this process is sufficient to reduce interstitial contamination levels below
performance-limiting thresholds. Further reductions in interstitial contamination can be realized
through extended processes to lower temperatures. Given the stronger driving force for impurity
segregation and precipitation at low-temperatures, a slow cooling rate (2 - 4 'C/min) from the
gettering plateau temperature effectively reduces the final impurity concentration (Figure 6.2,
Green).
A low temperature anneal (LTA), (Figure 6.2, Blue) further capitalizes on the
-
increased driving force at reduced temperatures, by annealing material at approximately 550
68
600
0 C,
and has proven effective at substantially reducing gettered interstitial iron
contamination.34,
1
1,
900__
_
850
ES'
0
_
_
_
_
_
_
_
_
_
Temperature Anneal
-Low
-Slow
Cool
-Standard
Process
1
800
750
700
.a650
Exponential
600
550
Cooling to
-Room
5Temperature
0
60
120
180
240
300
Time [min]
Figure 6.2: Gettering Time-Temperature Profiles
A standard high-throughput gettering process, shown in red, includes unloading
material directly at the plateau temperature. Significant reductions in the final
interstitial contamination after gettering however, can be realized through the
application of a slow cool, shown in green, or a low temperature anneal, shown in
red.
The application of the slow cool or LTA processes do not significantly influence the doping
profile of phosphorus in the wafer. The model of Bentzen et al,124 was applied to simulate the
doping profile following Fick's law in Equation (6.1). D(C) is the concentration dependent
diffusivity of phosphorus in cm 2 /s, and is defined in Equation (6.2).124 Following ref, 124 the first
term of Equation (6.2) defines the diffusivity of phosphorus in silicon through an interstitial
mechanism, while the second term defines the diffusitivity of phosphorus through a vacancy
mechanism. At low P concentrations, the interstitial mechanism dominates, while at high P
69
concentrations, the vacancy mechanism dominates. 2 4 The corrected value of Do = 5.8x 10-5 cm 2 /s
for interstitial diffusion is applied within this model,12 4 as identified by the author. 125 Figure 6.3
shows the simulated doping profiles obtained with standard (red), and both slow cooling and
LTA (both represented by blue) gettering processes. The addition of either a slow cool or LTA
does not appreciably change the doping profile of the n+ region.
a -=
at
(6.1)
-
ac)
ax ( D(C) ax
Pi
PV'
1022
E
0.
0
0
(6.2)
+Dl
I
1()Dq" + D q"
-
10 2 1
STD Gettering
Slow Cool & Low Temperature Anneal
1020
10Q19
P
----------------
------
10Q18
10Q 17
- - -- - - - - - - - -
a
a
a
I
I
I
I
I
I
I
1015
C
0.2
0.4
0.6
x [pim]
0.8
1
Figure 6.3: Calculated Phosphorus Diffusion Doping Profiles
124
Phosphorus doping profiles calculated using the model of Bentzen et al. , 125
Standard gettering is shown in red. Because of the low-temperatures employed,
the doping profile of phosphorus does not change appreciably with the addition of
a low temperature anneal after slow cooling (both represented in blue).
70
6.3 Getterability of Impurities
Not all impurities can be effectively removed during gettering. 0 ,
126
Molybdenum for
example, with its low diffusivity,1 18 has not been observed to effectively getter even after
extended treatments.8 0 , 127 An impurity must have a sufficient diffusivity to move to the benign
area to be effectively gettered. 3 9 Figure 6.4 a shows the diffusivity (a) and solubility (b) of
common impurities as a function of inverse temperature.1 18,
128,
129 Generally, slowly diffusing
species have lower solubilities. The resulting diffusion length for these impurity species are
shown for a 25 minute 845 *C process in Figure 6.4, c. A wafer is on the order of 102 Pm thick,
suggesting that only species that diffuse at a rate of magnesium or faster are readily getterable at
standard temperatures. Furthermore, substitutional diffusors may also be non-getterable because
of their low diffusivities.126 The solubility of a species at the gettering temperature (Figure 6.4)
defines an upper limit for the level of interstitial contamination expected after gettering.
Unfortunately, some non-getterable impurities also have a large lifetime impact (Figure 3.2),
indicating that some impurities must be effectively controlled during growth for high lifetime
material. 130
71
1018
10-3
10-4
-Co
10-5
10-6
10-7
1010-9
E
U
rz-
NiC
Fe
Mn
-
S016E
n
1015
1014-
10-10
W
10-12
FNi
\cr
1012
10-14
Ti
Co
101 I
10-15
5
10
15
20
25
4
Inverse Temperature [1 0 /K]
5
C)
25
20
10
15
Inverse Temperature [1 0 4/K]
d)
104
1018
1017
103
1016
-
E 102
1015
0 .6-1 101
0
100
-)
1013
10-1
4)
1012
0
1oll
r
Fe
1013
W
10-13
.2
Mn
U)
10-11
a
Cu
4
1014
liir
-2
IV
CuNiCoFeCrMnV TiMoWZn
CuNiCoFeCrMnV TiMoWZn
Figure 6.4: Diffusivity and Solubility of Metal Impurities
The diffusivity (a) and solubility (b) of metal impurities varies strongly with
species and is a strong function of temperature. 118 During a 25 minute gettering
process at 845 'C, some species have an inadequate diffusivity to be effectively
gettered (c). The solubility of a species at the gettering temperature of 845 *C
helps indicate the upper limit for interstitial contamination after gettering (d).
6.4 Furnace Maintenance
The level of doping achieved during diffusion was observed to decrease over the course of
multiple experiments with the furnace used (Tystar Tytan 3800) within this thesis. Multiple
72
sources of this process shift were investigated and included 1) an inadequate seal on the furnace
tube to provide slight positive pressure, 2) an improper calibration of the thermocouples used to
maintain the furnace temperature, 3) an expired liquid POC13 source, 4) buildup of residue within
the furnace tube (Figure 6.5, a), and 5) buildup of liquid droplets within the tubing used to
connect the POC13 source to the furnace tube (Figure 6.5, b). After repairing or checking items 14 with the furnace manufacturer, the liquid droplets within the tubing were removed (Figure 6.5,
b). The liquid was expected to have migrated into the tubes through the inadequate refrigeration
of the POC1 3 source and condensation of the warm gas on cooler tubing walls due to the lack of
thermal grease within a thermocouple well. In addition to the repairs above, a reduced level of
insulation was installed in the furnace tube to increase the tube exit temperature to reduce residue
build-up (Figure 6.5, a), and new procedures for furnace programing were implemented to
reduce challenges in the future (detailed in Appendix B). After these changes, the doping levels
provided by the furnace were observed to increase.
73
a)
b)
Figure 6.5: POC1 3 Furnace Contamination before Cleaning
Deposits of P 2 0 5 powdered solids were observed within the furnace tube (a).
Liquid deposits were also observed in the gas tubing that connects the POCl 3
source to the furnace tube (b).
74
CHAPTER
7
DEFECT IDENTIFICATION IN
As-GROWN EPITAXIAL WAFERS
7.1 As-Grown Lifetime and Interstitial Iron Concentration
The as-grown lifetime of p-type epitaxial material was measured after A12 0 3 passivation
(Section 5.1). A very low effective lifetime < 1pVs was observed for as-grown p-type material at
an injection level of 1015 cm 3 (Figure 7.1). This result is far below expectation, given the low
structural defect density of the material. An interstitial iron concentration ([Fei]) of (4.5
X1011
77)
cm 3 was measured via injection level dependent lifetime spectroscopy after 20 flashes
from a Semilab WT2000 flash lamp (Hensel EHT 3000, Section 4.4). A Fei concentration of
4.5x1011 cM- 3 is relatively low, and well below the concentration required to degrade lifetime to
<1 ps. SRH modeling 38 indicates a defect lifetime of TFei-Bs = 56 ps for this concentration, far
above the observed lifetime. However, the significant uncertainty of the measurement indicates
that this measurement is near its detection limit. Furthermore, the low lifetime of the material
suggests the presence of a substantial concentration of [Fei], which requires substantial energy
input for the dissociation of Fei-B, pairs which may not have been achieved with flash-lamp
illumination (Figure 4.9). Therefore, additional measurements were completed to elucidate the
root-cause of the low lifetime in as-grown material.
75
1.00
=L
0 0.75
E
5 0.50
00.25
000
-Effective lifetime: Gen II
As-Grown
Fe-B, associated
1013
1014
1015
Injection Level [cm4J
1016
Figure 7.1: As-Grown Lifetime p-type Material
The effective as-grown lifetime of p-type material is less than 1 ps. The Fei
concentration of 4.5 x 1011 cm-3 determined by lifetime spectroscopy is inadequate
to reduce lifetime to < 1 ps.
7.2 Bulk Mass Spectrometry
Injection level dependent lifetime measurements are sensitive to defects distributed
interstitially in the bulk of the wafer. A majority of metal contamination however, is usually
distributed in precipitates. Inductively coupled plasma mass spectrometry (ICP-MS, Fraunhofer
+
Center of Silicon Photovoltaics) was completed to measure the total concentration (interstitial
precipitate) of various species: Fe, Cr, Cu, Ni, Ti, Zn, W, Mo, V, Co, Mg, Al, Mn, Zr, Ag, Sn,
Au, Nb, P, and B. Four samples were provided for measurements: Gen I epi, Gen II epi, a float
zone control wafer directly from the box with saw damage, and a float zone control wafer that
was laser-cut following the sample preparation procedure used for the epitaxial samples. In all
cases but Gen I material, > 2g of material was provided to allow for two measurements with ICPMS. Very-high concentrations of many contaminants were measured in both the epi samples and
76
float zone controls. This casts doubt on the absolute concentrations of detected impurities, but
still allows for relative comparisons. Significant relative concentrations of Zn, Fe, Ti, Al, and Mg
are observed in the material. Zn, Fe, and Ti are significant lifetime limiting defects in p-type
silicon (Figure 3.2).38
7.3 Synchrotron micro x-ray fluorescence
ICP-MS measurements provide the total concentrations of impurities. However, the
distribution of impurities in addition to their total concentration provide valuable information
about the nature of defects.' 3 ' Micro x-ray fluorescence (p.-XRF) measurements were completed
by colleagues at beam line 21D-D at the advanced photon source (APS) synchrotron on structural
defect etched
32
material. p-XRF is sensitive to precipitates composed of impurities with a K x-
ray absorption edge <10 keV. Of the species measured with ICP-MS, this excludes detection of
the heavier elements of W, Mo, Zr, Ag, Sn, Au, and Nb.1 3 Precipitates of Ti, Fe, Cr, Cu, and Zn
are observed in as-grown p-type material (Figure 7.2). Of these species, Zn, Fe, and Ti were
observed in high total concentrations from bulk mass spectrometry and corroborate these results.
However, these results do not indicate absolute concentration as the solubility of these species
differs over five orders of magnitude (Figure 6.4, b) and an insignificant sample area was
analyzed with the tool. The highest precipitate concentration observed [pg/cm 2 ] is Ti, which also
has the lowest solubility of the species measured (Figure 6.4).
The size of iron precipitates is measured from the
-XRF data assuming spherical precipitates
assuming a volumetric density of precipitated iron of CFep= 2.6x 1022 cm- 3
134-136
The maximum
iron precipitate radius of approximately 16 nm is consistent with the observed range of iron
precipitate radii found in as-grown mc-Si materials.1 37 An increased density of small precipitates
may be present in the epitaxial material however, as the minimum precipitate radius observable
77
with the technique is approximately 8 nm. 138 The radius of the peak Ti precipitate was assessed
using the same method as applied for Fe.1 34 -136 The volumetric density of Ti precipitates were
calculated by adjusting CFep based on the volume of single Ti and Fe atoms using their respective
atomic radii.1 39 The resulting CTip = 1.8x 1022 cm-3, and maximum precipitate radius is
approximately 58 nm. While significantly larger than the observed size of Fe precipitates, this
value falls below the size of foreign inclusions. 136
78
Ti
Max: 2.86
Min: 0.00
Cr
Max: 0.0571
Min: 0.0000
Fe
Max 0.109
Min: 0.000
Max 0.0433
Min: 0.0000
4.
Max: 0.0175
Min: 0.0000
Zn
-
-
High [pg/cm2
Low [pg/cmj
10 pm
Figure 7.2: Synchrotron X-Ray Florescence Analysis of As-Grown p-type Material
Impurity precipitates of Ti, Fe, Cr, Cu, and Zn are observed in as-grown p-type
material.
79
7.4 Oxygen
Oxygen forms a recombination active defect in boron doped p-type silicon that is metastable
with illumination. 79 Oxygen in epitaxial silicon may diffuse from the substrate, or from
inadequate removal of the SiO 2 before initiating deposition. 140-142 However, the oxygen
concentration in epitaxial silicon is low (< 1017 cm-3 ) in the literature.1 40-1
42 This
is below normal
values for Czochralski (CZ) single-crystal silicon,1 4 3 and is not sufficient to degrade lifetime to
the observed as-grown level.144
7.5 Response to Gettering and High-Temperature Annealing
Useful conclusions about the nature of as-grown lifetime-limiting defects can be drawn from
assessing the gettered performance of the material. As detailed in the next chapter, lifetime
improves by >>100x after gettering. This indicates that a getterable defects limit as-grown
performance. A comparison with the diffusivities of point defect impurities (Figure 6.4) suggests
that V, Ti, Mo, or W cannot be responsible for the poor as-grown performance as these species
should not respond to gettering.
Conversely, of the initially considered impurities limiting as-grown lifetime, the effectiveness
of gettering Pt. Fe, Cr, and Ag is demonstrated.8 0
118,126
The gettering response for Zn, however,
is less well known.1 2 6 Zn diffuses by a kick-out mechanism,'"8 and may be influenced by the
density of structural defects.1 45 Pt and Au however, have similar diffusion mechanisms and
8
solubilities as Zn and are also gettered by phosphorus diffusion." '1145,146
80
7.6 Deep Level Transient Spectrometry
Deep level transient spectrometry (DLTS, V.P. Markevich and A.R. Peaker, University of
Manchester) measurements and analysis where completed on as-grown p-type material to
identify the species and concentrations of electrically active majority carrier traps in the lower
half of the bandgap. 1 mm diameter Al+Ti Schottky diodes were used on p-type material with an
Au rear contact. Figure 7.3 shows the DLTS spectra of as-grown material at two probing regions
(depths, W in pm) at a rate window of 80 s-. Two peaks are observed in Figure 7.3. Two levels
within the bulk of the sample are observed at E,+0.09 eV and Ev+0.32 eV.
-- Chip_1idl
low2 k
[Fe] egion)
Chip 2, d2
(low . high [Fe] region)
0.12 -
0.10
0.08
EO32 eV
EE+.O9 eV
0.04
0.00
-
0.02
40
60
80
100
120
140
160
180
200
220
240
260
280
300
Temperature (K)
Figure 7.3: DLTS Spectra for As-grown p-type Material
The DLTS spectra for as-grownp-type material shows two distinct peaks at two
probing regions (W) in the sample. The peaks correspond to energy levels of
Ev+0.09 eV and Ev+0.32 eV. Figure printed with permission from V.P. Markevich
and A.R. Peaker.
81
The E,+0.09 eV trap is observed at a concentration of approximately 3.5x 1013 cm~ 3 in the
sample bulk, while the
Ev+0.32 eV trap is observed at a concentration of approximately
4.5x 1013 cm-3. Both the Ev+0.09 eV and Ev+0.32 eV traps lie within literature values for the
double donor and single donor states of Pt in Si.118 Pt is sometimes used as an electrode material
in anodic etching of porous silicon, and has been observed in large concentrations after porous
silicon formation in the literature. 14 7
The Ev+0.09 eV trap is also near to the expected position of the shallow donor state of FeiBS.1 03 This confounds the carrier lifetime spectroscopy measurement of [Fei] = (4.5
77)x10"
cm-3. However, the large uncertainty of the [Fei] measurement with carrier lifetime spectroscopy
indicates that the measurement is near detection limits for the very low lifetime of the sample
considered.
The E,+0.32 eV trap is also near the expected donor trap level of Ag in ref,148 however
literature values generally fall between Ey+0.34 eV and Ev+0.40 eV.149 Ag is observed in
significant concentrations in the ICP-MS analysis and is an interstitial diffusor that responds to
phosphorus diffusion gettering. 126 The Ev+0.32 eV trap is also near literature values for the donor
levels of Mo and W, however, the high lifetime of the material after gettering excludes the
possibility of 4.5x101 cm-3 contamination from either of these impurity species given their poor
gettering response (Figure 6.4).
7.7 As-Grown Performance Limit Assignment
Injection level dependent lifetime, bulk mass spectrometry, micro x-ray fluorescence, and
deep level transient spectrometry measurements were completed to identify the underlying
performance-limiting defect. The comparison of the defect concentrations obtained in the above
82
measurements and the gettering response of the material indicates that Pt, Fe, Cr, Zn, and Ag
contamination may limit as-grown lifetime. Fe, Cr, Zn, and Ag contamination was measured in
significant bulk concentrations via mass spectrometry and observed to form precipitates via
micro x-ray fluorescence. Ag and Pt are undetectable with 10 keV x-ray, while Pt was also not
measured with bulk mass spectrometry. DLTS analysis indicates Pt as the primary electrically
active defect. It is a known impurity species in some porous silicon 147 and responds positively to
gettering.1
8
Electrically active contamination with concentrations of 3.5x 101" cm- 3 to 4.5x10"
cm-3 is sufficient to reduce lifetime to the observed levels in as-grown material (Figure 3.2).
7.8 Defect Distribution in As-Grown Epitaxial Wafers
Attributes of epitaxial wafer growth encourage a high density of as-grown interstitial
contamination, which limits as-grown lifetimes but improves gettering response (Chapter 8).
With iron for example, in traditional ingot silicon only 0.1% to 10% of the total contamination is
found as interstitial point-defects in an as-grown wafer.1 50 This encourages higher as-grown
lifetimes as the vast majority of contamination is maintained in the more benign precipitated
form. The rapid cooling of an epitaxial wafer from growth temperatures at 60 aC/min,5 3 in
comparison to ingot materials at a much slower 2-3 *C/min,15 1 provides less time for interstitial
impurities to find favorable precipitation locations. Furthermore, the rate of precipitation of iron
39,
is observed to increase with the density of structural defects 152 and oxygen precipitates.
153
In
epitaxial material however, a low density and size of oxygen precipitates are observed in the
literature.141,153 Figure 7.4 indicates this phenomenon with a schematic for the evolution of point
defects during cooling in both epitaxial (a) and traditional mc-Si (b) growth. During growth
point-defects are-distributed amongst structural defects. As wafer cooling begins, the solubility
83
of point-defects falls, and precipitation begins at favorable nucleation locations. Although both
materials may have a similar concentration of total contamination, the increased time available
for precipitate nucleation and growth, and the increased density of favorable nucleation locations
result in a reduced point-defect concentration after cooling. A reduced proportional concentration
of interstitial contamination leads to better as-grown lifetimes.
Metal Impurity Evolution During Wafer Growth
a) Epitaxial Wafer
Precipitation
Above Solubility
Wafer During Growth
StructuralDePap
t
*
Defect
*
0
Precipitation
Point Defect
b) mc-Si
Above Solubility
Wafer During Growth
$4
0
*
,
9
9
*)~*
4
'9
9
~~s
Precipitation
;X
09
9
~
Ak
wL
9
0
0
9
Solubility> Total
Contamination
mc-Si
2
.30C/min
*~
Solubility< T
otal
Contaminati on
-
E
WI
F__
---Time
(not to scale)
Figure 7.4: Evolution of Impurities during Wafer Growth
Material during growth contains impurities that are distributed as interstitial
defects. During wafer cooling, precipitation occurs at heterogeneous nucleation
sites. Epitaxial material (a) has a lower density of favorable sites and is cooled
more quickly than mc-Si (b), leading to a higher proportion of total contamination
found as point defects. The time axis is not to scale.
84
The effect of the cooling rate on the final point-defect concentration is modeled for Fe with
Ham's law 5 4 in Equation (7.1) as modified by Hieslmair et al 35 for numerical computation. The
time constant for precipitation, F precipitation [s], (Equation (7.2)) is observed to increase with
decreasing diffusivity, Di [cm 2/s] (Equation (7.3)) from Istratov et al,10 3 precipitation site
density, n [cm-31,
53
, 155 and the precipitate radius rt [cm]. The solubility of interstitial iron, and
the driving force for precipitation, is shown in Equation (7.4) from Istratov et al.10 3 The second
order effect of the changing precipitate radius is considered in Equation (7.5), with c, = 2.6x 1022
cm-3, the density of iron in
s-FeSi 2 precipitates. 135,156 Figure 7.5 shows the simulated [Fei] for an
epitaxial (a) and mc-Si (b) material with an initial iron concentration of 3.5x 1013 cm- 3 during
cooling from growth with n = 108 cm- 3 following ref,1 57 and initial precipitate radii of ro
=
1.5 x 10-6 cm for mc-Si and epi fit to the final as-grown radii from synchrotron pt-XRF
measurements.
37
In epi, the final interstitial iron concentration of [Fei] = 2.7x l0'
cm-3
corresponds to 77% of the total iron found as Fei in the as-grown material. This is far higher than
the final concentration of interstitial iron found in the mc-Si material, [Fei] = 1.6x10" cm-',
which corresponds to a interstitial proportion of 0.5% that aligns with experimental observations
in as-grown mc-Si material. 50
[Fe] +A = (S - [Fe,]t 1 - exp
At
(7.1)
precipitation
17
precipitation
4
1
nr(7.2)
D. = 1.0 x 10-3 exp - 0.67
S kB1
S = 8.4 X 1025 exp
-28
(7.3)
74
(kBT
85
_ [Fe, ], - [Fe, ]t-t
3
(7.5)
/3 )MnCP
Because of the rapid cooling of the epitaxial material, a low density of precipitate iron is
expected in as-grown material.15 3 This however, suggests that external gettering should be very
effective in the material as shown in Hieslmair et al'53 and Hofstetter et al,158 as the impurity
species are not strongly bound. Indeed, excellent gettering response is observed (Section 8.2).
86
a) Epitaxy
1,100
Temperature
1014
1,000
E
C
o
900
101 3[e
800
d
700
U
1012
11
L
10
Fe Solubility
0
500
400
10
5
Time [min]
b) inc-Si
-
1014
Temperature
E
e
-[Fe
1013
-
900
a.
800
$
1,000,
-
.2
a
E
600
1012
700
600
Epi
cooling
Lrate
1011
500
Fe Solubility
400
'
0
W
C,
E
100
Time [min]
200
Figure 7.5: Epitaxial Growth Iron Precipitation Simulation
Based on the work of Ham,1 54 Hieslmair et al,'3 5 and Haarahiltunen et al,1 57 the
proportion of total iron contamination that is found as interstitial point defects in
as-grown epitaxial material (a, 77%) is much higher than as-grown mc-Si material
(b, 0.5%). This depresses as-grown lifetimes in epi, but provides a strong
gettering response.
53
As observed in (b), epi is cooled much more quickly after
growth.
With the same model,' 3 5 ' 154 the as-grown iron distribution in epi is calculated as a function of
wafer cooling rate in Figure 7.6. The current cooling rate of 60 *C/min provides an as-grown
proportion of Fei of 77%. Decreasing the wafer cooling rate to 1 'C/min is predicted to reduce
the as-grown Fei proportion to < 0.1%, however such a modification would significantly reduce
the throughput of wafer growth by adding approximately 490 minutes to the growth cycle-time.
Further increases in cooling rate, however, may provide an appreciable increase in throughput
87
without significantly increasing the as-grown Fei proportion. Given the observation that the
material is gettered effectively because of its low density of heterogeneous nucleation sites and
high proportion of as-grown Fei (Section 8.3), rapid wafer cooling during growth is a viable
mechanism to increase performance of gettered material.
100
.0 75
C
U.
d0
0
CL
0
0
20
40
60
80
Cooling Rate from Growth [OC/min]
100
Figure 7.6: As-Grown Iron Distribution as a Function of the Wafer Cooling Rate
The as-gown distribution of iron is calculated as a function of wafer cooling rate
for epitaxial material using the model of Ham 15 and Hieslmair et al. 13 The asgrown proportion of Fei can be reduced through slowing the cooling step from the
growth temperature. However, this would negatively impact growth throughput
and economics, and may also reduce the gettered performance of the material.
7.9 Contamination Source
As-gown contamination with Pt, Fe, Cr, Zn, and Ag are suggested by ICP-MS, synchrotron
p-XRF, and DLTS. Fe and Cr are found in large concentrations (atomic percent) in stainless
steel, while Fe and Zn are found in large concentrations in galvanized steels. Ag is used in cell
metallization, while Pt can be used as an electrode in porous silicon etching.
88
In traditional ingot crystallization, or kerfless techniques that utilize a growth from melt, the
strong segregation coefficient (keff 10~ to 10-', meaning 10-8 to 10-5 of the impurities within a
melt are incorporated during solidification) of many metals impurities between the solidifying
'
crystal and the adjacent melt provides a significant driving force for contamination reduction.79
159
While the epitaxial process benefits from never being in direct contact with a crucible wall, it
does not benefit from this strong segregation to a melt during growth. Therefore, unintended
contamination of process gases and equipment may have a more detrimental impact than in
traditional growth from melt growth techniques.
89
CHAPTER
8
GETTERING RESPONSE
8.1 Material
As-grown p-type (boron doped) epitaxial wafers were provided with a thickness of <110 pm
and crystal orientation of <100> from two generations of furnace growth equipment, herein
referred to as Gen I and Gen II. The second generation of growth includes improved impurity
management and greater automation. Doping concentrations were determined as 0.50 a-cm for
Gen I material and 1.79 Q-cm for Gen II material using a four point probe (Cascade Microtech
probe, Keithley 4200). Average sample thickness was determined from separate measurements
of sample area with a flatbed scanner at 1200 dpi (Epson Perfection V700) and sample mass
(Ohaus Discovery). As-grown samples were exfoliated manually from their growth substrates
and laser-cut into approximately 4 x 4 cm2 samples. Residual porous silicon was cleaned from
the sample with a chemical polishing etch (CP4).
8.2 Lifetime Improvement with Gettering
The as-grown lifetime of the p-type material is inadequate for high-efficiency solar cell
operation. Phosphorus diffusion gettering was employed to increase lifetime to level that could
enable cell efficiencies > 20%.
90
8.2.1 Phosphorus Diffusion Time Temperature Profiles
Two processes were employed to reduce the concentration of electrically active point
defects. 160 First, a high-throughput standard process consisting of furnace loading at 800 0 C
followed by a 25 minute plateau at 845 *C and furnace unloading immediately at 845 *C was
applied. The plateau stage consists of three stages of gas flow. First, the PSG is formed during
POC13 deposition. Then two steps consisting of a nitrogen purge and oxidation are completed. A
second extended process consisting of a low temperature anneal (LTA) was employed to test for
the continued presence of [Fei] and available gains from additional optimization of the standard
process.160 Low temperature anneals have been demonstrated to effectively reduce the
concentration of Fei contamination.1,
1
Based on the work of Rinio et al,12 3 a 2 hr LTA at 575
'C was employed after free cooling at approximately 2.6 *C/min from the plateau temperature. A
nitrogen ambient surrounds the sample during the cooling and LTA steps. After gettering,
emitter sheet resistances of 75+11/-20 Q/sq and 77+10/11 Q/sq were measured on the front and back
of single samples from the standard and extended gettering processes respectively using a four
point probe.
8.2.2 Lifetime and Iron Contamination after Gettering
After standard phosphorus diffusion gettering of Gen I material, lifetime improved to 12
ps at an injection level of 1015 cm 3 on a 52 pm thick sample, and a [Fei] of (4.9
3.
The extended process resulted in a reduced effective lifetime of 9
1.2
23.9)x1010 cm~
0.9 ps and a [Fei] of (2.9
31.6)x1010 cm-3 on a 57 pm sample. The change in [Fei] between processes is within the margin
of error. The lifetime of the upgraded Gen II material increased markedly with the application of
phosphorus diffusion gettering. The standard process resulted in a reduced effective lifetime of
91
342
34 ps and a [Fei] of (2.5
0.76)x1010 cm-3 on a 95 pm sample. The extended process
resulted in an effective lifetime of 313
31 ps and a [Fei] of (3.2
7.4)x 109 cm-3 on an 80 pm
sample.
Table 8.1 summarizes the gettered lifetime results obtained on p-type material. The implied
open circuit voltage (Voc) are shown as well as the estimated bulk lifetime for the material. The
estimated bulk lifetime was calculated using a SRV of 8.1 cm/s calculated by comparing the
effective lifetime of a float zone control sample to intrinsic limits (Figure
5.3).69
The [Fei]
estimates herein have been updated from those displayed in ref,34 because of a modification to
the dissociated and associated fractions of [Fei]/[Fei-B,] following Tan et al, 108 a more accurate
estimate of ambient temperature during re-association, and a larger uncertainty (5%)94 in lifetime
for error bound calculations.
Table 8.1: Summary of Lifetime Results forp-type Material after Gettering
Sample
Gen L
Effective
Sample
Est. Bulk
Implied
Interstitial iron
lifetime at
Thickness
lifetime at
Voc at 1
concentration [cm~ 3 ]
1015 cm~ 3 [ps]
[pm]
1015 cm 3 [ps]
sun [mV]
12
1.2
52
13
9
0.9
57
9
1.3
647
(4.9
23.9)x1010
628
(2.9
31.6)x1010
(2.5
0.76)x1010
Standard
Gen I,
Extended
Gen II,
342
34
95
851
85
710
313
31
80
830
83
713
Standard
Gen II,
Extended
0.9
(3.2
7.4)x10'
After correcting for surface recombination, estimated bulk lifetimes > 800 ps are achieved
with Gen II material at An = 101 cm~ 3. This lifetime is sufficient to support advanced cell
92
architectures with efficiencies much greater than 20% and represents extremely high electrical
quality for a kerfless silicon substrate. Injection level dependent lifetime of Gen II material with
Fei dissociated and Fei-B, pairs is shown in Figure 8.1. The effective lifetime (a) of the standard
gettering material outperforms the extended gettering process. However, after correcting for
differences of surface limited lifetimes due to different sample thicknesses, the estimated bulk
lifetimes (b) are approximately equivalent. The strong signature of interstitial iron is observed in
the standard gettering sample with an expected crossover point near An
=
1014 cm3 and a large
response at high injection levels. Within a limited range of injection levels, estimated bulk
lifetimes over 1,000 ps are observed. The injection level for 1-Sun operation with a 50 Pm thick
cell is also indicated with the black diamond in the estimated bulk lifetime (Figure 8.1 (b)).
93
a) Effective Lifetime
400
=L 300
E
CO,
0-%
~200
easured
effective lifetimes
- STD Dissociated
* LTA Dissociated
SSTD Associated
SLTA Associated
0
100
1015
1014
Injection Level [cm-]
b) Estimated Bulk Lifetime
1,000
McPe
at
sun
1
D
50 pim cell
=L
700
E
I-
-J
.
400
inn
1014
O
o
*
stimated Bul.
lifetimes
* STD Dissociated
* LTA Dissociated
STD Associated
LTA Associated
1015
Injection Level
0
0
1016
[cm-31
Figure 8.1: Injection Level Dependent Lifetime for Gen Ilp-type Material
Effective lifetimes of> 300 ps are observed for Gen 1I wafers after gettering at An
= loll
cm 3 (a). The change in lifetime with Fei-Bs pairs dissociated and
associated, and the observation of a crossover point, indicates the presence of
interstitial iron in the standard gettering sample. The estimated bulk lifetimes are
shown in (b) with an estimated SRV of 8.1 cm/s. The lifetime impact of Fei-Bs
pairs is pronounced. The injection level corresponding to 1-Sun operation with a
50 pm cell is indicated in (b) with a black diamond.
After gettering,
estimated
lifetimes were compared to their theoretical
limits, or
"entitlement", to determine if lifetime is limited by interstitial iron. Figure 8.2 shows the
estimated bulk lifetimes of Gen II material and their concentration of Fei contamination
94
compared to the limiting lifetime due to unavoidable intrinsic mechanisms by the Richter et al
model 69 and Fei due to SRH modeling at An = 10" cm-3 38 With both Gen I and Gen II material,
the estimated bulk lifetime does not appear to be limited by interstitial iron because a) the
extended gettering processes which provided reduced [Fei] did not improve lifetime, and b) bulk
lifetime does not appear to be a function of the Fei limited lifetime. DLTS was again performed
on Gen II material after extended gettering. While numerous peaks were observed in as-grown
material, no detectable peaks are observed after standard (Figure 8.3), and extended (not shown)
gettering.
10,000
Limits due to Fei, Auger, and
radiative recombination at An
= 1015 cm-
-
I
LTA
Gen
11
STD
-
Gen
1,000
3
E
N
100
0 N%.
E
100
LU
0
------------------
10
- -----------
---- ----Gen ILTA
109
1010
1011
1012
1013
Interstitial Iron (Fe) Concentration [atomscm 3]
Figure 8.2: Performance Limits of Interstitial Iron
Estimated bulk lifetimes assuming a SRV = 8.1 cm/s and interstitial iron
concentration compared to theoretical limits at An = 1015 cM~ 3 . The lack of a
relationship between the theoretical Fei limited lifetime with the estimated bulk
lifetime, and the ineffectiveness of the extended gettering process at increases
lifetime indicate that the gettered material is not limited by Fei. Figure reprinted
from ref,3 4 with updated data.
95
0.12
-
010
As-grown (low T. high [Fe] region)
W from 1.30 sm to 1.57 pm (@ RT)
Get (low r, high [Fe] region)
W from 1.74 pm to 2.02 pm(@ RT)
E +O.32 eV
~008-
E,+0.09 eV
Standard gettering
eliminates
detectable traps in
material
S006
-p-type
004
-
002
0.00
-a
"I
40
60
80
100
I
120
I
140
I
I
160
I
180
200
I
220
240
260
I
280
300
Temperature (K)
Figure 8.3: DLTS Spectra of As-Grown and Gettered Gen 11 p-type Material
Various DLTS peaks are observed in the as-grown material (red). After standard
gettering, no detectable peaks are observed (blue). Figure printed with permission
from V.P. Markevich and A.R. Peaker.
8.3 Impact of Impurity Distribution on Gettering Response
As indicated in Section 7.8, the growth conditions of epitaxial material lead to a large
proportion of total contamination residing as interstitial species. This limits as-grown lifetime,
but enables excellent gettering response. Figure 8.4 shows the respective evolution of impurities
during gettering of epitaxial (a) and mc-Si (b) materials following Figure 7.4. In epitaxial
material, a large proportion of impurities in the as-grown state are distributed as interstitial pointdefects. These point-defects respond positively to external gettering, as they are not bound on
precipitates that resist the movement of the impurity species to the gettering layer (Figure 8.4,
a).1 58 Conversely, in mc-Si materials a large concentration of precipitates reduces gettering
effectiveness (Figure 8.4, b). Heterogeneous nucleation sites, such as dislocations, prove difficult
96
to getter 80
as the resulting strain field around the dislocation resists the driving force for
gettering.
Metal Impurity Evolution During Gettering
a) Epitaxial Wafer
As-Grown Wafer
*
0
During High-T Diffusion
o t.te~rinit
t*
LPrecipitate dissolution
During Cool-down
-ttI
Emitter
Bulk
LRe-precipitation
b) inc-Si
As-Grown Wafer
During High-T Diffusion
fie
0
A
During Cool-down
k
E
I-
MVP Time
Figure 8.4: Schematic Gettering Comparison of Epi and mc-Si Materials
As-grown material contains impurities that are distributed as interstitial pointdefects and precipitates. Because of the difficulty of gettering impurities from
heterogeneous nucleation points such as dislocations, 80' 81 the bulk of epitaxial
material (a) is more easily gettered of deleterious impurities than mc-Si (b)
material.
Controlled pre-anneals of epitaxial material to increase the proportion of precipitated iron on
the onset of gettering were completed following the work of Henley et al,161 Haarahiltunen et
al,1 62 and Hieslmair et al, 53
' As-grown material was annealed in a nitrogen ambient at 500 'C for
30 min, 90 min, and 1,260 min. Based on ref1 6 1 and ref,1 62 a 90 minute anneal is expected to
97
effectively increase the precipitated iron concentration. After annealing, the as-grown lifetime of
all wafers persisted at values < 1 ps. However, after standard gettering, a significant dependency
on pre-annealing duration and lifetime is observed (Figure 8.5, a). Effective lifetimes after Fei-B,
dissociation at an injection level of 10
sample, 56
5
cm-3 are 115 + 12 ps with the no pre-annealing control
5.6 ps with the 30 minute pre-anneal sample, and only 35
3.5 ps with the 90
minute pre-anneal sample. Corresponding increases in [Fei], summarized in Table 8.2, after
gettering are observed with increasing pre-annealing time and reduced lifetime (Figure 8.5, b).
As precipitated impurities at heterogeneous nucleation sites prove difficult to getter, 80'
81
the
observed trend in post-gettering lifetime suggests that the gettering response of epitaxial material
should be high due to its favorable as-grown distribution of more readily-gettered point-defects.
Also included in Table 8.2, are the results of the sample annealed for 1,260 minutes. With this
sample however, the lifetime after gettering improved significantly to 199
20 ps after gettering.
Although the wafer was not intentionally doped, the extended duration at an elevated
temperature in the POC13 furnace may have provided the wafer with effective external gettering
due to residual phosphorus in the furnace. Other experiments, at higher temperatures but much
shorter durations, have indicated the ability of residual phosphorus to diffuse into wafers during
annealing treatments.
Table 8.2: Summary of Lifetime Results for Pre-Annealed p-type Material after Gettering
Pre-Annealing
Effective lifetime at
Interstitial iron
Duration [min]
1015 cm 3 [ps]
concentration [cm~ 3]
0
115
12
(8.6
2.3)x1010
30
56
5.6
(4.3
0.56)x 10
90
35
3.5
(8.5
1.0)xI0r
199420
(1.1
1.2)x10l
1,260
98
a)
200
Effective Lifetime
FerBs dissociated
no anneal
=L 150
pre-annealing
reduces gettered
W.
E
100
lifetime
***
30 min anneal
.0-.-e
min anneal
+90
*5
kA A
A
AA AAAAAA.
-
o 50.*
0
1016
1015
1014
Injection Level [cm-31
b)
200
=L
-
~Gettered
Fe]
-
150
.
1012
E
E--
.1011
M 100
O
GeIered
50
0
Lifetime
0
- - . .. . .. . .
20
40
.. . 1011
-. . .
. . ..
60
80
100
Pre-annealing Time [min]
Figure 8.5: Impact of Pre-Annealing on Gettered Lifetime
Increased duration of pre-annealing at 500 'C in a nitrogen atmosphere is
observed to reduce lifetime (a,b) and increase the concentration of Fei after
gettering (b). In b, 10% absolute error is shown for the QSSPC lifetime data,
while error in [Fei] is calculated assuming random propagation of errors through
the [Fei] calculation with a QSSPC lifetime repeatability of 5%.94
To further identify the cause for lifetime improvement with gettering, epitaxial samples were
also annealed with the same time temperature profile used by the standard gettering process at
845 0 C, but without the flow of POC13 gas. A sheet resistance of 183+38 /- 37 Q/sq was provided by
the process, indicating that a small amount of residual phosphorus was present in the tube,
providing a small effect of external gettering. Lifetimes after the high temperature anneal
remained low at < 1 lis (Figure 8.6), suggesting that an impurity species that responds to external
99
gettering with a phosphorus source is responsible for the as-grown lifetime limit. The result also
indicates that external gettering dominates the lifetime improvement observed during gettering.
A Fei concentration of (7.8
46)x 101" cm 3 was measured on the no POC1 3 control sample. This
is within the error of the Fei concentration of (4.5
77)x10" cm~3 measured on as-grown
material, but as with the as-grown sample, an inadequate concentration to warrant the < 1 ps
lifetime of the material.
0.60
0.50
Minor lifetime
S0.40
improvement with
n no
POCI 3 anneal
.90.30
-J
0.20
0.10
0.00
1013
No POC 3 anneal, associated Fe-B
0-As-grown, associated Fe-B,
1014
1015
1016
Injection Level [cm-3
Figure 8.6: Lifetime in p-type Material after High Temperature Anneal without POCl 3
The lifetime of material did not appreciably improve from the as-grown state
(blue) after completing a high temperature anneal (red) following the timetemperature profile of a gettering process but with phosphorous doping. The lack
of a response indicates that external gettering plays a dominant role in the
gettering response of the material.
8.4 Root Cause of Lifetime Change between Generations
Determining the root cause for the large change in effective lifetimes between generations of
wafer growth validates the effectiveness of system changes. It also can elucidate the persisting
100
causes for the discrepancies between the achieved lifetimes of Gen II material in comparison to
the theoretical entitlement (Figure 8.2). The bulk lifetime of the material is defined by Equation
(8.1). The bulk, intrinsic, and Fei limited lifetimes of Gen I and Gen II material after standard
gettering are summarized in Table 8.3, allowing the lifetime of the unaccounted for defect(s),
Tdefect,
that is responsible for the underperformance of the both materials to be determined. The
materials have a different intrinsic lifetime due to a change in doping concentration. The
resulting rdefect is 13 ps for Gen I material, and rdefect is 1,120 ps for Gen II material. In both
materials, the estimated bulk lifetime is dominated by the impact of
Terect.
rdefect
for Gen II
material is 100 times higher than rdefect of the Gen I material.
1
Tbulk
_
1
'intrinsic
1
1
rdefect
Tstructural
1
1
Z Fei
'rdefect
+
(8.1)
1
1
'slow impurity
"other
(8.2)
Table 8.3: Lifetime Parameters for Gen I and Gen I1p-type Material
Tbulk
at
Tintrinsic
at
TFei at
Resulting rdefect at
Sample
1015 cm- 3 [ps]
1015 cm- 3 [Vs]
1015 cM-3 [Vs]
1015 cm-3 [ps]
Gen I, Std
13
1,030
885
13
Gen II, Std
851
8,990
5,750
1,120
The root cause of Tdefect may be caused by decorated structural defects, contamination from
slowly-diffusing difficult to getterable impurities, or another defect type (Equation (8.2)).
Structural defects are one possibility. Epitaxial kerfless grown on a porous release layer has
average structural defect densities of 5104 cm 2 .54 , 16,
17
Structural defects were revealed with a
Sopori etch,13 2 and analyzed over a 1 cm 2 and 4 cm 2 portion of a Gen I and Gen II sample
respectively with optical microscopy (Nikon Eclipse LV100, NIS-Elements software). In line
101
with previous measurements, an average structural defect density of approximately 104 cm-2 was
observed for both generations of material. Similarly, the percent wafer surface covered by high
structural defect density (>105 cm-2 ) is low (< 5%) and comparable between generations. 160 The
low area coverage of defective regions, a predictor of performance loss, 84'
163
in epitaxial kerfless
silicon compares favorably to standard ingot multicrystalline material (Figure 8.7). Given the
similarity of the structural defect distribution between epitaxial generations, structural defects are
concluded to not be the root cause of performance difference between generations.
a)
Std. mc-Si
b) Epi Kerfless
Figure 8.7: Comparison of Structural Defects in Std.
mc-Si and Epi Silicon
Epitaxial kerfless silicon (b, right) has a lower area coverage of structurally
defective regions than standard multicrystalline material (a, left).84 '163
Difficult to getter impurities are another potential source of performance loss. Bulk mass
spectrometry indicates significant concentrations of impurities (Ti, Mo, V) that may prove
difficult to getter and are recombination active (Figure 6.4). Additionally, significant reductions
are observed in the concentration of Mo (82% reduction to detection limit) and V (59%
102
reduction) in Gen II relative to Gen I material (Figure 8.8). Decreased concentrations of Cr and
Nb are also observed in Gen II material. However, the impact of Cr is considered negligible
because Cr has similar gettering characteristics as Fei,1 2 6 and such effective Fei extraction was
observed in both Gen I and Gen II materials.
!"" 500
o 400
300
C
0
U 200
C
S100
C 100
0
$'
o
0
Figure 8.8: Reduction in Impurity Contamination in Gen II Material
Gen II material exhibits a reduced concentration of Mo, V, Cr, and Nb
contamination relative to Gen I material. Comparison with SRH recombination
modeling indicates good agreement between measured concentration reductions
in Mo and V and the lifetime improvement in Gen II material.
Figure 8.9 shows the estimated bulk lifetime (SRV = 8.1 cm/s) of Gen I material after
standard gettering compared to the SRH 38 and intrinsic 69 lifetimes for the know level of Fei
)
contamination from lifetime spectroscopy and Mo (1.2x1013 cm-3 ) and V (1.5x1013 cm-3
contamination from bulk mass spectrometry. Literature suggests that the interstitial, electively
active, concentrations of Mo are equal to 15% to 100% of the total Mo concentration. 164, 165 A
good fit is observed between the estimated bulk lifetime (red circles) and modeled lifetime with
an assumed 40% of Mo and V (widely-dashed black line) residing as an interstitial species
103
(Figure 8.9). A poor fit is obtained if 100% of Mo and V are electrically active (tightly-dashed
grey line), suggesting some precipitation has occurred. At high injection levels, the modeled
curve for a 40% interstitial fraction deviates from the acquired data. This deviation can largely be
attributed to increased uncertainty of the effectiveness at high injection levels. As seen in Figure
5.3, the SRV of the passivation increases rapidly at high injection levels. When this is accounted
for, a better fit is obtained. These results indicate that a reduction in the concentration of Mo and
V during growth is the root cause of the lifetime improvement observed in Gen II material. The
high concentrations of these difficult to getter impurities observed in Gen I material would not
support the > 1 ms estimated bulk lifetimes observed in Gen II material.
50
Variable SRV
a45RI
C
E
D
40
SRH + Intrinsic lifetime:
[FeJ = 5.6x1010 cm- 3
35
[Mo 4 .] =4.8x1012 cm
[V 4A =6.oxo12 cM-
30
325
0
E
LI-
20
15
1
Estimated bulk lifetime:
-Gen I STD Gettering
Fe-B, dissociated
Constant SRV=8.1 cm/s
10
5
-
0
104
[FeJ = 5.6x1010 cm- 3
3
[MoJo
= 1.2x10 c[.. V= 1.5X1013 cm
1015
1016
Injection Level [cm-3]
Figure 8.9: SRH Modelling of Gen I Material after Gettering
A good fit is observed between the estimated bulk lifetime (red circles) and the
modeled SRH and intrinsic lifetimes of the material with contamination from Fei,
Mo, and V with 40% of the Mo and V contamination is electrically active (black
line). The lifetime of the material excludes the possibility of 100% electrical
activity of the Mo and V contamination (grey line). 10% absolute error is shown
for the QSSPC lifetime data following Blum et al.94
104
CHAPTER
9
DEFECT IDENTIFICATION AND
MITIGATION IN N-TYPE MATERIAL
n-type wafer doping provides numerous advantages for high-lifetime silicon substrates, and is
employed the highest efficiency commercial cells available. 166 n-type silicon has an overall
lower sensitivity to metallic impurities, especially Fei, (Figure 3.2). Additionally, metastable
defects with boron are eliminated through the absence of boron doping including: oxygen-boron,
iron-boron, and chromium-boron. However, it is noted that a lifetime of approximately 3 x p-type
material is required for n-type material for roughly equivalent carrier diffusion lengths, the
spatial embodiment of lifetime.'
06 This
discrepancy arises because of lower carrier diffusivity in
n-type vs. p-type silicon. As with p-type material above, lifetime and spectroscopy
measurements are employed to identify defects in n-type material.
9.1 As-Grown Lifetime and Defect Identification
The as-grown lifetime of n-type matches the behavior ofp-type material at < 1 ps. Figure 9.1
shows the injection level dependent lifetime for as-grown n-type material. As with the p-type
material, the root cause for the poor lifetime of as-grown n-type material is assessed. However, a
reduced subset of tools are employed, though general trends from ICP-MS measurements may
still apply with the n-type material.
105
1.00
0.75
0.50
0.25
0*
Measured effective lifetime:
0.00 -as-grown
1013
n-type
1014
1015
1016
Injection Level [cm- 3]
Figure 9.1: As-Grown Lifetime n-type Material
As with p-type material, the as-grown performance of n-type material is less than
1 ps. A stronger injection level dependence of lifetime is observed in n-type in
comparison to p-type material.
DLTS measurements and analysis were also completed (V.P. Markevich and A.R. Peaker,
University of Manchester) on as-grown n-type material to identify the species and concentrations
of electrically active majority carrier traps in the upper half of the bandgap. 1 mm diameter Au
Schottky diodes were deposited on the sample with an Al rear contact. One strongly distinct trap
is observed. Figure 9.2 shows the DLTS spectra of as-grown material at two probing regions at a
rate window of 80 s-1.
106
010
Ctp1, d2 (ovt regon)
Cp2, d2 (tghT region)
-
0.08
E,-0.23 eV
0.06
0.04
0.02
0,00
40
60
80
100
120
140
160
180
200
220
240
260
280
300
Temperature (K)
Figure 9.2: DLTS Spectra for As-grown n-type Material
The DLTS spectra for as-grown n-type material shows a distinct peaks of E--0.23
eV at two probing regions (W) in the sample. Figure printed with permission from
V.P. Markevich and A.R. Peaker.
An Arrhenius analysis identified the observed trap in n-type material at an energy level Ec0.23 eV after correcting for an electric field dependence of the trap with Laplace DLTS analysis.
The E--0.23 eV trap is measured at a bulk concentration of 3.Ox 1013 cm 3 , and lies near the level
of Pt in Si.
18
The potential observation of Pt in n-type material suggests a common impurity
source for the contamination observed in p-type material (Section 7.6). The Ec-0.23 eV defect
also lies near expected values for Cri. 148,
167
The minor peaks observed in the spectra are
attributed to impurity complexes with hydrogen.
These results coupled with previous p-type ICP-MS measurements, gettering lifetime
response, gettered p-XRF, and SRH modeling indicate that Pt, Cr, Fe, Zn, and V may limit asgrown performance. Pt is most strongly suggested from DLTS measurements. Point-defect
contamination at a concentration of 3.5x1013 cm- 3 is adequate to reduce lifetime to < 1 ps in n-
107
type material (Figure 3.2). As with p-type material, the excellent response of the material during
phosphorus diffusion suggests that the deep-level defect unidentified in DLTS measurements is a
getterable species.
9.2 Lifetime Improvement with Gettering
Similar gettering methods to those employed for p-type material were expected to improve the
lifetime of n-type material. Phosphorous diffusion gettering is effective at gettering n-type
material by forming a heavily doped n+ region.3 9'
168
Standard and extended gettering processes
were again employed on the n-type material. Al 2 0 3 is also used again on n-type material for
surface passivation. A1 2 0 3 effectively passivates n-type silicon through a reduction of the
interface defect density and the formation of an inversion layer with large amount of fixed
negative charge in the film.90 This mechanism provides excellent results, however, over a limited
range of sheet resistances (emitter, 100-200 Ohm/sq ) A1 2 0 3 is not effective at passivating n-type
material.' 1 6 As in p-type material, gettering removes detectable traps from DLTS (detection limit
approximately 3 x 1011 cm 3) in n-type material (Figure 9.3).
108
0.08
007
E-0.23 eV
-
0-.-
As-grow-11C; Chip2, d2 (high T region)
Gettered-11 F; Chip3, d2 (high T region)
0.06
0.05
0.04
Standard gettering
eliminates
0.03 -detectable
0.02
traps in
n-type material
--
0.01
0.00
40
60
80
100
120
140
160
180
200
220
240
260
280
300
Temperature (K)
Figure 9.3: DLTS Spectra after Standard Gettering of n-type Material
As in p-type material, phosphorus diffusion gettering removes detectable traps
from DLTS in n-type material (detection limit approximately 3x 1011 cm 3). Figure
printed with permission from V.P. Markevich and A.R. Peaker.
Figure 9.4 shows the injection level dependent lifetime of a 120 pm thick n-type silicon
sample after gettering. The effective lifetime is 878
88 ps at an injection level of An = 10" cm-
3. This lifetime provides a minority carrier diffusion length on the same order as the > 300 ps ptype results. Additionally, it should also be capable of supporting cell efficiencies well over 20%.
The intrinsic lifetime limit is shown for comparison in Figure 9.4,69 and used to calculate a SRV
assuming that the bulk wafer performance is equal to the intrinsic limit. In this case, additional
corona charge was used to increase the strength of the inversion layer (Figure 5.4). The resulting
SRV of < 3 cm/s is exceptional and among the best results obtained with A12 0 3 passivation. 90
109
1,600
5
Intrinsic Limit
1,400
4
2-1,200
3
1,000
2
2i
-J
lifetimer
"
800 80Effective
600..
0
1014
1015
1016
Injection Level [cm-3]
Figure 9.4: Injection Level Dependent Lifetime of Gettered n-type Material
Effective lifetimes > 850 pis are obtained with n-type material after gettering.
Exceptional surface passivation is provided by corona-charged A1 20 3 with a
resulting SRV < 3 cm/s.
9.2.1 Comparison to Intrinsic Limits
The exceptional results for surface recombination (SRV < 3 cm/s) indicate that the observed
lifetime is a strong function of bulk performance, as better passivation performance relative to
that achieved cannot be expected. In this case, the SRV calculation indicates that the actual bulk
lifetime of the material is equal to the intrinsic limit. Therefore, no unintended contaminant
dictates the material lifetime. As shown in Figure 9.5, a decrease in the doping concentration of
this material could increase the achievable lifetime to > 10 ms over its current level (red dotted
line) based on the Richter et al model for intrinsic lifetime.69
110
Resistivity Doping
100
[ohm-cm] [cm-]
--
E
---
Decreasing doping
increases intrinsic limit
4.59
1X1015
0.99 5x10 15
0.53 1X1016
0
= 10
Feagured95
nrni
Lftm
ii n-type Material
The intrinsic lifetime of material is a strong function of doping concentration. The
material shown in Figure 9.4 (red dotted line) has a high doping concentration that
limits its performance due to unavoidable intrinsic mechanisms. The lifetime of
the material could therefore be increased significantly with a change in doping
concentration.
9.2.2 p-XRF Measurement of Gettered n-type Material
As with as-gown p-type material, pt-XRF measurements were completed on gettered n-type
material by colleagues at beam line 2ID-D at the APS synchrotron. Impurity precipitates of Cr,
Zn, and Fe were again observed (Figure 9.6). Additional precipitates of Co and Ni were also
observed. The precipitates observed in n-type material are larger than those observed in p-type
material. The size of precipitates were again measured from the p-XRF data assuming spherical
precipitates following references.' 34~136 An iron precipitate radii of approximately 13 nm is
similar to the observed size in p-type material. Other impurity species were found in larger
precipitates. The density of Cr and Zn precipitates [atomns/cm 3] were determined by adjusting the
density of iron precipitates based on the volume of a single impurity atom using the atomic
111
radius.139 The resulting densities are
CFep=
2.6x10
cm-3, Ccrp = 2.1X10
Cm-, and CZnp
3.4x 1022 cm- 3, 135 and precipitate radii of 38 nm for Cr and 26 nm for Zn. These radii fall below
the size of foreign inclusions.1 36 As mentioned above, stainless steel has large concentrations
(atomic percent) of Fe and Cr, while galvanized steel has large concentrations of Fe and Zn. The
presence of precipitates after gettering suggests that further high-temperature processing could
reduce lifetime through the dissolution of existing precipitates.138
112
Cr
CO
Ni
Max: 1.07
Min: 0.01
Max: 0.0915
Min: 0.0185
Max: 0.658
Min: 0.000
Fe
Fe
Max: 0.0512
Min: 0.0015
Max: 0.027
Min: 0.000
High [pg/cm2
Low [pg/cm2
-.
1 pm
Figure 9.6: Synchrotron X-Ray Florescence Analysis of Gettered n-type Material
Cr, Zn, Co, Fe, and Ni precipitates are observed in n-type material after gettering.
The size of the Fe precipitate is comparable to the as-grown p-type material.
113
CHAPTER
10
CONCLUSIONS
10.1 Conclusions
Significant innovation is required to reach the module price goal of $0.501W by 2020 put
forth by the U.S. DOE SunShot program. Epitaxially grown kerfless silicon could play an
important role in achieving this target by reducing silicon utilization while enabling highefficiency devices. Herein, screening criteria for high-impact research projects was presented,
and then used to explore the benefits of epitaxially grown kerfless silicon in comparison to
historic kerfless growth techniques. Then, the identification of lifetime-limiting defects in asgrown p-type epitaxial material, and the mitigation of those defects through improvements to
both growth and processing were presented. Lastly, lifetime-limiting defects were identified in ntype material, and gettering response was assessed.
Conclusions include:
Cost Modeling Elucidates Fruitful Innovation Areas
Power conversion efficiency is the strongest influencer of manufacturing cost. Opportunities
to reduce cost through material or process substitution, such as kerfless wafers, must be weighed
against any detrimental effects on cell efficiency. A "cheap and dirty" strategy won't necessarily
be cheap, especially if the costs of the entire PV system are not considered.
Excellent Surface Passivation is Required to Study High-Lifetime Material
114
Excellent surface passivation (< 5 cm/s) is needed to measure high effective lifetimes (> 1
ms) on 100 pim thick high-quality materials such as epitaxial kerfless silicon. A1 2 0 3 deposited by
thermal ALD provided such passivation. However, even with excellent SRV values of 3 cm/s,
many hundreds of ps of effective lifetime are lost to surface recombination with thin wafers.
As-Grown Epitaxial Material Suffers from Unique Performance Limits
Poor lifetime was observed in as-grown material. The root cause was identified as
contamination by impurities with a strong response to gettering. Rapid cooling discourages
precipitation of impurity species, and leaves a larger proportion of the total impurity
contamination in an electrically active form. However, excellent gettering response is observed
due to the high interstitial proportion in as-grown material.
Improving As-grown Material is an Effective Method to Increase Gettered Lifetime
All impurities are not easily getterable. Therefore, there is a limit to the lifetime improvement
that can be realized during processing, so steps should be taken to minimize contamination
during growth. The lifetime improvement in Gen II material is due to a reduction in ungetterable
impurities present in Gen I material. Because epitaxial silicon does not benefit from solid to
liquid impurity segregation during growth, extra care must be employed to limit contamination.
Epitaxially Grown Kerfless Silicon can Support High-Efficiency Devices
Effective lifetimes > 300 ps and > 800 ps were achieved with p-type and n-type material
respectively at injection levels of 1015 cm 3 after gettering. Estimated bulk lifetime for both
materials approached or exceeded 1 ms. This lifetime is sufficient to support cell efficiencies
well greater than 20%, and shows that a material use saving technology can provide an efficiency
increase.
115
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Appendix
A
COST MODELING RESOURCES
High-Level Perspectives
D.O.E. SunShot Vision Study http://www1.eere.energy.gov/solar/pdfs/47927.pdf
Prof. Varun Rai at UT Austin http://www.estresearch.com/
Ryan Wiser at LBNL http://eetd.lbl.gov/people/ryan-wiser
Innovation and Cost Modeling
NREL/MIT U.S. China Study http://pubs.rsc.org/en/content/articlelanding/2013/ee/c3ee4O7Olb
NREL Module Roadmap http://www.sciencedirect.com/science/article/pii/S0927024813000457
SEMI Cost of Ownership Standard http://www.pvgroup.org/node/3201
SEMI PV Innovation Roadmap http://itrpv.net/Reports/Downloads/
MIT Modeling Efforts http://pv.mit.edu/TMA/
Photon International equipment surveys, and Photovoltaics international magazine
Green Tech Media Research Webinars (released with new reports, give plenty of information)
Installed / System Economics
NREL Installed Cost Analysis http://www.nrel.gov/docs/fyl2osti/53347.pdf
System Advisor Model (Cost of Electricity) https://sam.nrel.gov/
126
Appendix
B
GETTERING STANDARD
OPERATING PROCEDURE
Recipes
As part of the changes made during the maintenance of the POCl 3 furnace in 2013, there are two
new standard recipes. They have variable inputs for the temperatures and durations of certain
steps, and should provide us with the flexibility we need without having to make a new recipe for
most cases. Because of the variables, good bookkeeping is essential to record what inputs were
made when the recipe was used. Both load at 700 *C, a more gentle temperature for both the
furnace and operator, and consist of a few other enhancements for reliability and maintenance.
They also have two-stage diffusions, were a high flow period is meant to saturate the tube with
POC1 3 and a low flow period is meant to reduce turbulence and improve uniformity.
The first recipe is STD ISTEP.002. It includes one fixed temperature diffusion, and allows for
ramping down to an unload temperature. The next is STD2STEP.002. As you can imagine, this
provides two fixed temperature diffusions, i.e. LTA. The directions below to run the recipes do
not substitute for training by the tool owner.
Loading and running STD1STEP.002 and STD2STEP.002
Select recipe load on the POC1 3 furnace computer and select the new recipe (always loading the
recipe fresh will allow you to confirm the recipe parameters). When the recipes load, they will
prompt you for the value of the variables within the program. If you make an error, re-load the
program. Figure B.I shows a schematic of the input variables described below.
127
a) STDISTEP.002
900
PTMP: Plateau Temperature, example 845 oC
850
E. 800
STME: Extra Soak Time
7
after Required N 2 soak,
example 10 min
2700
. 650
oad at
700 0C
600
550
500
UTMP: Unload
Temperature, Set
to PTMP for
-direct
unloading,
,exampleB 8
0
b) STD2STEP.002
900
30
60
Time [min]
0
OC
90
120
PTMP: Plateau Temperature, example 845 OC
ATME: Annealing time at
ATMP, example 20 min
STME: Extra Soak Time
after Required N 2 soak,
example 10 min
80
2a 800
___
700-----4)70
0. 650
E
ATMP: Annealing
Temperature,
example 750 C
60 Lo ato ATMP for
C
-
550
500 10
30
60
Time [min]
UTMP: Unload
Temperature, Set
direct unloading,
example 7000C
90
120
Figure B.1: Schematic of Standard Recipe Time Temperature Profile Variables
1. Loading STD 1 STEP.002
a. PTMP - plateau temperature. You must enter XXX.X i.e. 845.0 for each of the 3
zones. Each zone should match. The minimum is temperature is 750 0 C and the
max is 1110 C (for the new baffle set) for this step or the run will be aborted.
b. STME - extra soak time in addition to the baseline 6 minutes used in
program. Do not put 0 for short times, put 00.00.01. A time of 0 will mean the
step will not end. Refer to POC1 3 application guide for sheet resistance targets for
the plateau temperature and time that are specified. The total plateau time for the
baseline process is 25 minutes.
c.
UTMP - unload temperature. The furnace will ramp to this temperature before
unloading. The stabilization counter is 1 minute, so if you are unloading at you
128
plateau temperature, then it will add 1 minute. The run will be aborted if the
temperature is greater than 850*C after stabilization.
2. Running STD1STEP.002
a. Turn on the furnace cooling fans.
b. To run, select the "Run" button. This will ramp the furnace up to the load
temperature to avoid having to use "Recipe Modify".
c. Start and complete RCA cleaning while the furnace is heating up.
d. Once stable at 700 "C, select the Event button to open the furnace door.
e. Acknowledge the alarm and load samples when the door has opened fully.
f.
Select Event again to close the furnace door. The recipe will automatically begin.
g. Once stable at the unload temperature, an alarm will sound. Acknowledge the
alarm and use the event button to open the door.
h. Unload samples by putting wafer boat on the piece of glass under the furnace tube
to cool.
i.
Select Event again to close the furnace door. The furnace will automatically go
back to idle.
j.
After the furnace is cool, turn off the cooling fans.
3. Loading STD2STEP.002
a. Set the first 3 variables as directed for STDlSTEP.002 above.
b. This recipe adds another variable ATMP - annealing temperature. The recipe
will ramp to the second plateau defined by ATMP as quickly as it can from the
plateau step. You can again specify an unload temperature.
c. This recipe also adds another variable ATME - annealing time. Set the length of
the annealing plateau. Again, do not put 0 for short times, put 00.00.01. A time of
0 will mean the step will not end. Refer to POC1 3 application guide for sheet
resistance targets for the plateau temperature and time that are specified.
4. Running STD2STEP.002
a. Follow the process for STD1 STEP.002 above.
129
Custom Recipes
Please follow/modify to the following guidelines if your experiment allows:
1. Do not load at a temperature higher than 700 *C. Remember that the experimental
evidence has shown that the load temperature does not affect results (but it probably does
effect the lifetime of the furnace).
2. When opening the furnace door, keep the set temperatures 100 - 200 *C below the load
temperature to avoid having the heating elements stressing to keep the tube hot with the
door open.
3. When the furnace is hot (> 700 C), please don't run the loader faster than 15 ipm. It was
hard to pin down what an appropriate value for this is from Henry. But again if you
experiment can handle it, and the faster speed was to avoid waiting, then I think reducing
the boat speed will reduce furnace stress.
4. Do not unload over 850 *C. Henry is already uncomfortable with 800 *C, so I don't think
we should push too hard hear unless really warranted.
Chemical Cleaning of Sample for Gettering with RCA
Multiple chemical cleaning procedures are available to use before gettering. RCA cleaning is
likely the most conservative process.
1. Start heating the furnace up to the load temperature as described above. If necessary
remove saw damage or other wafer defects with a CP4 etch described in the next
appendix.
2. Pre-rinse all beakers and the thermometer
3. Fill beakers
a. HF Dip (approximately 3% by volume in DI)
b. RCA1 (350 ml DI, 70 ml ammonium hydroxide, 70 ml hydrogen peroxide)
c. RCA2 (350 ml DI, 70 ml hydrochloric acid, 70 ml hydrogen peroxide)
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4. Heat RCA beakers to 70 C on hotplates (I usually set the hotplates to about 250 0C until
the temperature reads approximately 60 C, and then back down the temperature to
around 70 0C)
5. Process
a. Initial DI rinse one time to remove dust from sample
b. Initial HF Dip (To remove initial oxide)
c. DI Rinse three times in plastic DI beaker
d. RCA1 70 *C, 10 minutesDI Rinse three times in DI (1) beaker
e. HF Dip
f.
DI Rinse three times in DI (1) beaker
g. RCA2 70 0C, 10 minutes (during this, make a fresh HF bath)
h. DI Rinse three times in DI (2) beaker
i.
Final HF Dip (To remove RCA2 contaminants and prepare for gettering)
j.
DI Rinse three times in DI (2) beaker
k. Blow dry samples with N2 gun
1.
Unload sample boat with dedicated "after RCA" tweezers into POC1 3 furnace
wafer boat. (It is preferred to have a buddy do this and start the gettering process
to reduce the duration elapsed between the final HF dip and loading the samples
into the furnace).
6. Start the furnace program (or have a buddy do it) as described above.
7. Properly dispose of waste, rinse all beakers 3x, and store them in cabinet
After Gettering
1. Let wafers cool.
2. I think it is always worth completing a lifetime test on the WCT-120, and rough lifetime
map with the Semilab WT-2000 after gettering. The data may prove useful in the future.
3. If the wafers are p-type, measuring sheet resistance using the Keithley 4200 and the four
point probe may also prove valuable.
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Appendix
C
&
EMITTER REMOVAL
WAFER THINNING WITH CP4
Emitter removal & Wafer Thinning with CP4
CP4 etches (removes) silicon while smoothing a sample. Emitters should be removed before
passivation and lifetime measurements with both p-type and n-type samples. The initial process
was provided by Dr. Jasmin Hofstetter.
1. Pre-rinse all beakers and the thermometer
2. Fill beakers
a. HF Dip (approximately 3% by volume in DI)
b. CP4 (540 ml nitric Acid, 180 ml acetic acid, 72 ml hydrofluoric acid)
c. KOH 30% (in solvents hood, the KOH is pre-mixed and contained in a re-sealable
white frosted bottle under the solvents hood. It is labeled KOH 30%)
3. Process
a. DI Rinse once in plastic DI beaker to remove dust
b. CP4 (duration depends on number of samples and target removal. I use 2 minutes
to remove emitters, and 4 minutes to remove -15 um total from a wafer)
c. DI Rinse three times in plastic DI beaker
d. HF Dip
e. DI Rinse three times in plastic DI beaker
f.
KOH 30%, 10 minutes (this steps remove some left over contamination from the
CP4 etch)
g. DI Rinse three times in plastic DI beaker
h. HF Dip
i.
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DI Rinse three times in plastic DI beaker
j.
Unload samples (If a RCA clean is followed by this, Restarting with another HF
dip is conservative. Before whatever final HF dip is employed (After RCA2 for
POCl3 or after RCAl for ALD), it is advised to make a fresh mixture of HF to
reduce contamination).
4. Properly dispose of waste, rinse all beakers 3x, and store them in cabinet
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Appendix
D
AToMIC LAYER DEPOSITION
STANDARD OPERATING PROCEDURE
The initial chemical cleaning and ALD processes were provided by Dr. Jasmin Hofstetter and
Dr. David Fenning.
Chemical Cleaning for ALD Aluminum Oxide
1. Pre-rinse all beakers and the thermometer
2. Fill beakers
a. HF Dip (approximately 3% by volume in DI)
b. RCA1 (350 ml DI, 70 ml ammonium hydroxide, 70 ml hydrogen peroxide)
c. RCA2 (350 ml DI, 70 ml hydrochloric acid, 70 ml hydrogen peroxide)
3. Heat RCA beakers to 70 *C on hotplates (I usually set the hotplates to about 250 0C until
the temperature reads approximately 60 *C, and then back down the temperature to
around 70 *C)
4. Process
a. Initial DI rinse one time to remove dust from sample
b. Initial HF Dip (To remove initial oxide)
c. DI Rinse three times in plastic DI beaker
d. RCA1 70 *C, 10 minutes (during this, make a fresh HF bath)
e. DI Rinse three times in DI (1) beaker
f.
HF Dip
g. DI Rinse three times in DI (1) beaker
h. RCA2 70 0C, 10 minutes (during this, begin heating the DI post beaker on the
hotplate that held the RCA1 beaker)
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i.
DI Rinse three times in DI (2) beaker
j.
Blow dry samples with N2 gun
k. Unload sample boat with dedicated "after RCA" tweezers into dedicated "after
RCA" sample box
5. Properly dispose of waste, rinse all beakers 3x, and store them in cabinet
ALD deposition at Harvard CNS
1. Log into CNS system
2. If the chamber is very "flaky" vacuum the chamber with the vacuum stored next to the
sputtering tool on the floor. Remove the plastic fitting on the end of the hose, to avoid
melting the plastic in the chamber.
3. Set inner and outer ring temperatures to 200 0C
4. Load program "AL203_5nm_200C_Season" in software
5. Place Si chips in chamber in rough pattern for propping up samples with ceramic
tweezers
6. Run program
7. Load program "AL203_20nm_200C_Deposit" in software
8. Load wafers in chamber with ceramic tweezers while attempting to cover as little of the
chips as possible
9. Run program
10. Unload wafers into dedicated after ALD sample box
11. Unload chips into chip box
12. Reset furnace temperatures to default
13. Log out of CNS System
ALD annealing
1. Ensure the correct tube in placed in the furnace. The new tube, goes with the new
pushrod stored in the glass containment tube, and the new wafer boat stored in the plastic
box. Ultra high purity nitrogen (UHP300) should be used for the gas.
2. Set furnace to 350 0C
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3. Set N 2 flow rate to 5 SCFM
4. Uncover tube end
5. Remove boat from box on cooling plate while not touching the boat
6. Load samples in boat within furnace enclosure
7. When temperature has stabilized, load sample boat into end of furnace with push rod
located below furnace
8. Run for 10 to 30 minutes
9. Pull boat into cool in end of furnace gently (It takes me ~ 1min) and leave for 5 minutes
until cool
10. Unload samples into general sample box within furnace enclosure
11. Place sample boat on cooling plate back within box
12. Turn off furnace
13. When temperature reaches approximately 200 0C, turn off N 2 flow and place clean wipe
over the end of the furnace
14. Leave furnace enclosure doors closed when not in use, and leave air filter running
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