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Implementation of Advanced Solar-Cell Analysis at Cell Test Ronald - - PowerPoint PPT Presentation

Implementation of Advanced Solar-Cell Analysis at Cell Test Ronald A. Sinton, Adrienne L. Blum Wes Dobson, Harrison Wilterdink, Justin H. Dinger, Cassidy Sainsbury Sinton Instruments, Boulder, CO, 80301, USA A vision for end-to-end metrology


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SLIDE 1

Implementation of Advanced Solar-Cell Analysis at Cell Test

Ronald A. Sinton, Adrienne L. Blum Wes Dobson, Harrison Wilterdink, Justin H. Dinger, Cassidy Sainsbury Sinton Instruments, Boulder, CO, 80301, USA

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SLIDE 2

A vision for end-to-end metrology for electronic quality (1999 NREL Silicon Workshop)

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SLIDE 3

A vision for end-to-end metrology for electronic quality (2016 NREL Silicon Workshop)

Step Metric Fundamental Analysis Impact Analysis Feedstock τ vs. Δn τ vs. Δn Implied IV curve Crystal τ vs. Δn, Ω-cm, trapping τ vs. Δn Implied IV curve Wafer τ vs. Δn, Ω-cm, trapping τ vs. Δn Sorting Dopant diffusion τ vs. Δn, Ω-cm, trapping τ vs. Δn Implied IV curve Passivation τ vs. Δn, Ω-cm, trapping τ vs. Δn Implied IV curve Cell I, V, Rs, Rsh, τ vs. Δn, NA τ vs. Δn Real/pseudo-IV curve Module I, V, Rs, Rsh, τ vs. Δn, NA τ vs. Δn Real/pseudo-IV curve System I, V, Rs, Rsh, τ vs. Δn, NA τ vs. Δn Real/pseudo-IV curve

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SLIDE 4

Feedstock Qualification (Lifetime Test)

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SLIDE 5

Feedstock Qualification (Lifetime Test)

7.38 ms

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SLIDE 6

Suns-Voc Curves at the Array Level (3.6 KW)

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SLIDE 7

A vision for end-to-end metrology for electronic quality

Step Metric Fundamental Analysis Impact Analysis Feedstock τ vs. Δn τ vs. Δn Implied IV curve Crystal τ vs. Δn, Ω-cm, trapping τ vs. Δn Implied IV curve Wafer τ vs. Δn, Ω-cm, trapping τ vs. Δn Sorting Dopant diffusion τ vs. Δn, Ω-cm, trapping τ vs. Δn Implied IV curve Passivation τ vs. Δn, Ω-cm, trapping τ vs. Δn Implied IV curve Cell I, V, Rs, Rsh, τ vs. Δn, NA τ vs. Δn Real/pseudo-IV curve Module I, V, Rs, Rsh, τ vs. Δn, NA τ vs. Δn Real/pseudo-IV curve System I, V, Rs, Rsh, τ vs. Δn, NA τ vs. Δn Real/pseudo-IV curve

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SLIDE 8

Cell Test is Unique: 100% Testing of Wafers

We need to take maximum advantage of this opportunity! Device physics at cell test:

  • Lifetime vs. injection level
  • bulk lifetime and emitter saturation current densities
  • Relevant measurement of series resistance (Suns-Voc

curve)

  • Time response of high-efficiency cells (Capacitance)
  • Examples:
  • n-type high-efficiency solar cell
  • A study of p-type solar cells spanning low to high efficiency
  • Power loss analysis for record-efficiency cell
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SLIDE 9

R&D and Production Cell Testing

  • Laboratory cell tester
  • MultiFlash technology
  • Measures full IV curve with conventional parameters (Eff, Jsc,

Voc, Vmp, Jmp, FF)

  • Measures Suns-Voc (pseudo parameters, lifetime vs. injection

level, J0, BRR, lifetime at Vmp, dark Rsh, SUBSTRATE DOPING)

  • Production cell tester
  • Production cell tester (250 MW installed in production to

date)

  • All the same parameters
  • New SingleFlash technology enables high-speed testing
  • Potential for 4800 tests per hour
  • ~ 200ms cell test time of stationary cell
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SLIDE 10

Methodology: Outline

Parameter Method

IV parameters MultiFlash or SingleFlash technology; filtered Xenon light Substrate doping Time-dependent continuity equation Lifetime vs. excess carrier density Time-dependent Suns-Voc data using doping result Rs Evaluation of IV and Suns-Voc curves at Jmp Rsh Ohm-meter in dark at 0 Volts Voltage (Strategic, 6 points) 8 Channel simultaneous data acquisition Current same Intensity same (using silicon reference cell) Temperature RTD Capacitance effects Constant charge method ( EUPVSEC Dresden, 2006)

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SLIDE 11

Lifetime data: Everyone does this with test wafers and a lifetime tester

Vmp

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SLIDE 12

IV curves: BSF, PERC, n-type, Auger limit

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SLIDE 13

But it is the same thing! Lifetime data and IV data

∆𝑜𝑙 , ∆𝑜𝑙+1, ∆𝑜𝑙+2 …

Calculate Voltage:

𝑊 + 𝐾𝑆𝑡 = 𝑙𝑈 𝑟 ln 𝑂

𝐵 + ∆𝑞

∆𝑜 𝑜𝑗2

Calculate Recombination:

𝐾 = 𝑄ℎ𝑝𝑢𝑝𝑕𝑓𝑜𝑓𝑠𝑏𝑢𝑗𝑝𝑜 −𝑆𝑓𝑑𝑝𝑛𝑐𝑗𝑜𝑏𝑢𝑗𝑝𝑜 − 𝑊 𝑆𝑡ℎ (𝐾𝑙, 𝑊

𝑙), (𝐾𝑙+1, 𝑊 𝑙+1), …

Including series resistance and shunt

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SLIDE 14

IV in Terms of Emitters and Bulk Lifetime

𝑊 = 𝑙𝑈 𝑟 ln (𝑂

𝐵 + ∆𝑞)(∆𝑜)

𝑜𝑗2 − 𝐾𝑆𝑡 𝐷𝑣𝑠𝑠𝑓𝑜𝑢 = 𝑄ℎ𝑝𝑢𝑝𝑕𝑓𝑜𝑓𝑠𝑏𝑢𝑗𝑝𝑜 − ∆𝑜𝑟𝑋 𝜐𝑐𝑣𝑚𝑙 + 𝐾0𝑔𝑠𝑝𝑜𝑢 + 𝐾0𝑐𝑏𝑑𝑙 𝑂

𝐵 + ∆𝑞

∆𝑜 𝑜𝑗2 − 𝑊 𝑆𝑡ℎ

[Recombination]

“Thin-base limit”

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SLIDE 15

IV in Terms of Emitters and Bulk Lifetime

𝑊 = 𝑙𝑈 𝑟 ln (𝑂

𝐵 + ∆𝑞)(∆𝑜)

𝑜𝑗2 − 𝐾𝑆𝑡 𝐷𝑣𝑠𝑠𝑓𝑜𝑢 = 𝑄ℎ𝑝𝑢𝑝𝑕𝑓𝑜𝑓𝑠𝑏𝑢𝑗𝑝𝑜 − ∆𝑜𝑟𝑋 𝜐𝑐𝑣𝑚𝑙 + 𝐾0𝑔𝑠𝑝𝑜𝑢 + 𝐾0𝑐𝑏𝑑𝑙 𝑂

𝐵 + ∆𝑞

∆𝑜 𝑜𝑗2 − 𝑊 𝑆𝑡ℎ

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SLIDE 16

Rs Measurement Using Suns-Voc Curve

Rs from Suns-Voc does NOT depend on quality of fit to a model (no 1- or 2-diode equations or such nonsense)

Rsh=ΔV/ΔJ Rs=ΔV/Jmp

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SLIDE 17

Biggest Challenge with High-Efficiency n-type

Time response of high-efficiency cells (capacitance)

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SLIDE 18

Ramp-rate Artifacts (PC1D simulations)

0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 0.045 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Current Density (A/cm2) Voltage (V)

Modeled Si cell (Voc = 720 mV, thickness = 200 µm) IV curves at different ramp rates

Steady State 100 ms 50 ms 20 ms 10 ms (industry std) 5 ms 2 ms

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 20 40 60 80 100

Voltage/Cell Time (ms)

Voltage Ramp Rates

2 ms 100 ms

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SLIDE 19

High-Efficiency n-type Cells: 200X Higher Capacitance!

SunPower, Sanyo 2016

PERC Cell (3 Ω-cm) Standard Screen Print (1 Ω-cm)

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SLIDE 20

Eliminating errors due to slow time response during flash testing

Sol

  • lution: Test under constant ch

charge condit itions:

Measure V and J, while holding (V + J×Rs) constant using a feedback

  • circuit. 10 years of industrial production and R&D experience with this

technique.

  • R. A. Sinton, 21st EU PVSEC, (2006); pp. 634-638;

US patents 7696461 B2 2010, 7309850B2 2007

∆𝑜𝑙 , ∆𝑜𝑙+1, ∆𝑜𝑙+2 …

Calculate Voltage:

𝑊 + 𝐾𝑆𝑡 = 𝑙𝑈 𝑟 ln 𝑂

𝐵 + ∆𝑞

∆𝑜 𝑜𝑗2

Calculate Recombination:

𝐾 = 𝑄ℎ𝑝𝑢𝑝𝑕𝑓𝑜𝑓𝑠𝑏𝑢𝑗𝑝𝑜 −𝑆𝑓𝑑𝑝𝑛𝑐𝑗𝑜𝑏𝑢𝑗𝑝𝑜 − 𝑊 𝑆𝑡ℎ (𝐾𝑙, 𝑊

𝑙), (𝐾𝑙+1, 𝑊 𝑙+1), …

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SLIDE 21

Eliminating errors due to slow time response during flash testing

  • SintonDresden2006.pdf

Sol

  • lution: Test under constant ch

charge condit itions:

Measure V and J, while holding (V + J×Rs) constant using a feedback

  • circuit. 10 years of industrial production and R&D experience with this

technique.

  • R. A. Sinton, 21st EU PVSEC, (2006); pp. 634-638;

US patents 7696461 B2 2010, 7309850B2 2007

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SLIDE 22

Example: IV test of a high-efficiency n-type cell

High-Efficiency n-type

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SLIDE 23

IV-test example: N-type high efficiency

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SLIDE 24

IV-test example: N-type high efficiency

Bulk lifetime @ -ND Jof+ Job = slope*qni

2W

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SLIDE 25

IV-test Example: n-type High Efficiency

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SLIDE 26

Example: IV test of a PERC cell

PERC cell

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SLIDE 27

Example: IV test of a PERC cell

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SLIDE 28

IV-test example: PERC cells

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SLIDE 29

Lifetime and Substrate Doping Measurements of Solar Cells and Application to In-Line Process Control

Adrienne L. Blum Wes Dobson, Harrison Wilterdink, Justin H. Dinger, Ronald A. Sinton Sinton Instruments, Boulder, CO, 80301, USA IEEE PVSC, Portland, Oregon, 2016

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SLIDE 30

Measurement Samples: P-type Study

  • P-type cells processed with varying techniques:
  • Multi-crystalline Al BSF cells
  • High Performance Multi-crystalline Al BSF cells
  • Multi crystalline PERC cells
  • Monocrystalline PERC cells
  • Monocrystalline PERC cells
  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 31

Doping Measurement: An Opportunity

  • Substrate doping and t vs. Dn at the cell level
  • Substrate doping
  • Wafer position in ingot or brick  prediction of [O]/other

impurities  potential prediction of LID behavior

  • Information relevant to lateral series resistance in PERC cells
  • Gives final substrate doping, including changes from high

temp steps

  • Effective lifetime
  • Surface passivation quality
  • Substrate quality
  • Contamination during high-temperature processing
  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 32

Measurement Parameters

  • Analyze pVmp and efficiency dependence on

substrate doping (NA) and effective lifetime (τeff)

  • pVmp is used because the five groups of cells come from

different processing techniques, allows for a comparison independent of Rs

  • pVmp: 515-584mV
  • Efficiency: 15.8-21%
  • τeff: 5-100μs
  • NA: 5×1015-3×1016cm-3
  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 33

pVmp and Efficiency Correlate to τ×NA

  • Five different cell processing techniques all follow

the same trend

𝑊 + 𝑆𝑡𝐾 = 𝑙𝑈 𝑟 𝑚𝑜 𝐾𝑡𝑑 − 𝐾 𝑂

𝐵 + ∆𝑜)𝜐𝑓𝑔𝑔

𝑟𝑋𝑜𝑗2

mc-Si Al BSF mc-Si (high performance)Al BSF mc-Si PERC Mono PERC Mono PERC

10

16

10

17

10

18

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) tx NA (s/cm

3)

  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 34

pVmp and Efficiency Correlate to τ×NA

  • Five different cell processing techniques all follow

the same trend

𝑊 + 𝑆𝑡𝐾 = 𝑙𝑈 𝑟 𝑚𝑜 𝐾𝑡𝑑 − 𝐾 𝑂

𝐵 + ∆𝑜)𝜐𝑓𝑔𝑔

𝑟𝑋𝑜𝑗2

mc-Si Al BSF mc-Si (high performance)Al BSF mc-Si PERC Mono PERC Mono PERC

10

16

10

17

10

18

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) tx NA (s/cm

3)

10

16

10

17

10

18

14 15 16 17 18 19 20 21

Efficiency (%) tx NA (s/cm

3)

  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 35

Substrate Doping Doesn’t Tell the Whole Story

10

16

10

17

10

18

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) tx NA (s/cm

3)

10

15

10

16

10

17

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Doping (cm

  • 3)

1 10 100 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Lifetime at Vmp (s)

  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 36

10

15

10

16

10

17

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Doping (cm

  • 3)

1 10 100 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Lifetime at Vmp (s)

High Performance Multi Al BSF

  • pVmp ranging from 518.5 to 542.3 mV
  • Similar substrate doping ~7.5x1015 cm-3
  • No pVmp trend due to doping
  • Clear trend due to lifetime
  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 37

10

15

10

16

10

17

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Doping (cm

  • 3)

1 10 100 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Lifetime at Vmp (s)

Multi-crystalline Al BSF

  • pVmp correlation to both doping and lifetime
  • Indicating independent effects of these parameters
  • Expected for variations in wafers from multi-crystalline bricks
  • A. Blum et al. IEEE PVSC, Portland, Oregon, 2016
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SLIDE 38

10

16

10

17

10

18

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) tx NA (s/cm

3)

10

15

10

16

10

17

0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Doping (cm

  • 3)

1 10 100 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59

pVmp (V) Lifetime at Vmp (s)

Mono-crystalline PERC

  • Strong dependence on lifetime-doping product
  • No distinct correlation to lifetime or doping independently
  • Cause of high quality substrate
  • Bulk lifetime is very good
  • Lifetime-doping product is determined by the front and back J0e

𝑂

𝐵 + Δ𝑜 𝜐𝑓𝑔𝑔 =

1 1 𝜐𝑐𝑣𝑚𝑙 𝑂

𝐵 + Δ𝑜 + 𝐾𝑝 𝑔𝑠𝑝𝑜𝑢 + 𝐾𝑝 𝑐𝑏𝑑𝑙

𝑟𝑜𝑗

2𝑋

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SLIDE 39

Big Data: Simple Example

  • Measure 200,000 multi-crystalline cells (2 days of

data)

  • Resistivity varies from 1-3 Ω-cm
  • If you want to isolate the dependence on doping,

then:

  • Compare a histogram of cells with 10,000 1 Ω-cm

resistivity with a histogram of 10,000, 2 Ω-cm cells

  • The resulting distributions (for each) are independent
  • f the effects of doping, effectively isolating the

experiment to look at other variables

  • Differences between the histograms indicate doping

dependence

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SLIDE 40

Last Example: Energy-loss analysis at IV-test

Based on results published by SunPower at PVSC

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

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SLIDE 41

Example of use of lifetime data at cell test: 25.2% n-type cell (SunPower)

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

Cells used to demonstrate 24.1% module efficiency

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SLIDE 42

Loss analysis presented by SunPower at PVSC

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

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SLIDE 43

Loss analysis: Bulk Lifetime

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

Bulk lifetime (2.3%)

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SLIDE 44

Loss analysis: J0 (front and back)

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

J0f + J0b (2.3%)

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SLIDE 45

Loss analysis: Sum of Rs components

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

Rs (1.9%)

(Internal and grid Rs can be separated in R&D)

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SLIDE 46

Loss analysis: Fundamentatal recombination

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

Doping, Rs, and Vmp (0.2%)

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SLIDE 47

Loss analysis: Inactive area/edge effects

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

R&D, apertured IV + interpretation (0.9%)

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SLIDE 48

Loss analysis: Bulk Lifetime

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

Bulk lifetime (2.3%)

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SLIDE 49

SunPower analysis from PVSC paper Test wafers prior to IV test (statistically significant)

But the bulk lifetime extracted at cell test was 6.7 ms, not 20 ms! (use of lifetime vs. injection level data at cell test to determine bulk lifetime) =20 ms

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

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SLIDE 50

SunPower analysis from PVSC paper Test wafers including “patterning”

Going back and including “patterning” steps in the lifetime tests for the regions of interest on test wafers matches the bulk lifetime of the cell at cell test. =7 ms

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

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SLIDE 51

Loss analysis: Requires ext. spectral response + model

David D. Smith et al. “Silicon Solar Cells with Total Area Efficiency over 25%” IEEE PVSC June 2016.

Bulk lifetime = 6.7ms (2.3% power loss)

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SLIDE 52

Conclusions

  • Advanced device physics can be performed at cell test
  • Normal IV parameters + Suns-Voc, t vs. Dn, Rs, Rsh,

substrate doping

  • Enables sophisticated loss analysis, wafer by wafer, at

cell test

  • Fully implemented in line-speed production tools
  • Max speed 4800/hr (measurement time = 200ms). Limited

by wafer transport to 2400/hr at present

  • Big data enables resolution for discriminating efficiency

dependence on process control, substrate doping, surface passivation

  • Extends well-known device-physics tools to cells and

modules

  • Reliability studies (tvs. Dn) on cells as well as test wafers
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SLIDE 53

Acknowledgements

  • The authors would like to acknowledge Trina Solar

and the Photovoltaics Manufacturing Consortium (PVMC) from providing solar cells for this work

  • The information, data, or work presented herein

was funded in part by the United States Department of Energy Phase II SBIR, under award number DE-SC0010156