A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Implementation of a 

Comprehensive On-Line 

Closed-Loop Diagnostic System 

for Roll-to-Roll Amorphous 

Silicon Solar Cell Production 

Final Subcontract Report 

23 April 2003—30 September 2006 

T. Ellison 

Energy Conversion Devices, Inc. 

Troy, Michigan 

Subcontract Report 

NREL/SR-520-41560 

May 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337

Subcontract Report 

NREL/SR-520-41560 

May 2007 

Implementation of a 

Comprehensive On-Line 

Closed-Loop Diagnostic System 

for Roll-to-Roll Amorphous 

Silicon Solar Cell Production 

Final Subcontract Report 

23 April 2003—30 September 2006 

T. Ellison 

Energy Conversion Devices, Inc. 

Troy, Michigan 

NREL Technical Monitor: Richard Mitchell 

Prepared under Subcontract No. ZDO-0-30628-11 
 

This publication was reproduced from the best available copy 

submitted by the subcontractor and received no editorial review at NREL

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CONTENTS 

BACKGROUND AND EXECUTIVE SUMMARY............................................................................................v 

1. DEVELOPMENT OF CLOSED-LOOP CONTROL OF FILM THICKNESS...................................................1 

1.1 Introduction ........................................................................................................................1 

1.2 3rd Generation ITO/ZnO CLTC Systems............................................................................1 

1.2.1 Closed Loop Thickness Control ..............................................................................3 

1.2.2 Production Operation..............................................................................................4 

1.2.3 Further Evaluation and4 Future Plans.....................................................................4 

1.3 a-Si Thickness and Process Control...................................................................................5 

1.3.1 Introduction.............................................................................................................5 

1.3.2 Long-Term Measurements in the 25 MW Equipment ..............................................5 

1.3.3 Short-Term Quality Control (QC) using the PVCD CR Measurement......................8 

1.3.4 Conclusion and Future Work...................................................................................9 

2. COMPONENT CELL PV CAPACITIVE DIAGNOSTIC (PVCD) ............................................................13 

2.1 Introduction and Background............................................................................................13 

2.2 Uses of the PVCD System for Online QA/QC...................................................................17 

2.2.1 Past Uses of the PVCD System for Quality Assurance and Quality Control..........17 

2.2.2 Near-term future uses of the PVCD System for QA/QC ........................................23 

2.2.3 Longer Term PVCD Uses .....................................................................................24 

3. DEPOSITION OPTIMIZATION AND PLASMA DIAGNOSTICS...............................................................25 

 3.1 Introduction and Summary ...............................................................................................25 

 3.2 Investigation of the Uses of Plasma Diagnostic Systems..................................................25 

 3.3 Inline I-V Measurement System........................................................................................28 

3.3.1 Investigations of the Spire Measurement System .................................................28 

3.3.2 Cell Measurement Techniques – Effect of Brushes on the Material ......................32 

3.3.3 Sheet and Shunt Resistance and Effects on I-V Measurement.............................33 

3.3.4 LED Light Source Development/Extreme Reliability..............................................37 

3.3.5 Electronics Chassis ..............................................................................................40 

3.3.6 Software ...............................................................................................................41

4. YIELD IMPROVEMENTS...............................................................................................................42 

4.1 Summary..........................................................................................................................42 

4.2 Background......................................................................................................................42 

4.3 Cell Sorting:......................................................................................................................43 

4.4 Summary of Results of Indoor Studies .............................................................................44 

4.4.1 Initial Measurements (prior to accelerated testing) – Principal Findings ...............46 

4.4.2 Final Measurements (after accelerated testing) – Principal Findings ....................47 

4.4.3 Light Soak Tests...................................................................................................49 

 4.5 Results of Outdoor Testing...............................................................................................50 

 Future Work.............................................................................................................................51 

iii
List of Figures 

Figure 1 Schematic of the United Solar 30 MW/yr a-Si deposition equipment.........................................viii 

Figure 2 Side view of the United Solar 30 MW/yr a Si processor...............................................................ix

Figure 3 ECD/United Solar Joint Venture manufacturing capacity over the last 15 years and 

announced plans for future expansion ......................................................................................... x 

Figure 1.1 ITO Spectrometer Operator Display ............................................................................................. 2 

Figure 1.2 Schematic Layout of ITO CLTC Inputs and Outputs .................................................................... 2

Figure 1.3 CLTC Expert Display. Red boxes highlight the proportionality constants for the oxygen 

valves and PEMs .......................................................................................................................... 3 

Figure 1.4 Wavelength of the ITO Reflectance Minimum over a production run........................................... 4 

Figure 1.5 Top Cell Charging Rate signal from the PVCD in the 5 MW production machine recorded 

over a 25 day period in 2002 ........................................................................................................ 5 

Figure 1.6 Data Diagnostic Display (DDD) covering 250,000 m of operation showing a slow decrease 

in PV device charging rate signal from the PVCD [top plot, 5%/div] and correlated decrease 

in the relative thickness of the PV material from the Bottom, Bottom + Middle, and 

Bottom + Middle + Top cell reflection spectrometers [bottom plot, 1%/div].................................. 6 

Figure 1.7 Online and Offline data showing effect of using the online PVCD top cell CR signal to 

correct the cell thickness .............................................................................................................. 7 

Figure 1.8 Static Burns prior to cathode cleaning [top], after initial cleaning [middle], and standard 

Golden Profile [bottom] ................................................................................................................. 7 

Figure 1.9 Upper Plot: Top Cell Charging Rage, Offline Spire ISC, and Top Cell Quantum Efficiency 

Measurements; Lower Plot: Reflection Spectrometer Complete Device Thickness .................... 8 

Figure 1.10 Upper Plot: PVCD Bottom, Bottom + Middle, and Bottom + Middle + Top relative VOC, 

and Offline Spire VOC () and PMAX () ......................................................................................... 9 

Figure 1.11 Top Plot: circled region shows effect of 20 s static burn on spectrometer signals – the 

magnitude of the deviation is about 1%...................................................................................... 11 

Figure 1.12 New Mark Lycette design for in-situ process chamber spectrometer......................................... 12 

Figure 2.1 Close-up view section view of the PVCD sensor head, shutter, and transparent view of 

the “pusher plate”........................................................................................................................ 15 

Figures 2.2-3 Side and top sections showing the new Mark Lycette “Atrium Design” Component 

Cell PVCD................................................................................................................................... 16 

Figure 2.4 DDD screen capture showing the effect of the n-2 cathode outage on the online open 

circuit voltage measurement made by........................................................................................ 18 

Figure 2.5 DDD screen capture showing the on-the-fly correction of a PVCD-detected I3 cathode 

outage and the resulting improvement in product, and n3 thickness modulation 

experiments ................................................................................................................................ 19 

Figure 2.6 DDD display showing online and offline device measurements which are strongly 

affected by the change in Germane gas source......................................................................... 20 

Figure 2.7 DDD* display showing the United Solar product relative open circuit voltage, VOC ................... 21

Figure 2.8 Data Diagnostic Display showing the Online Bottom Cell VOC and Offline Spire 

complete device (triple-junction) ................................................................................................. 21 

Figure 2.9 Screen capture of online Data Diagnostics Display [6000 m FS................................................ 22 

Figure 2.10 Online data from a run where there were 5% cell thickness and web speed changes.............. 23 

Figure 3.1 Present blind spots: contamination at between the p and n (including ITO) layer ..................... 26 

Figure 3.2 Renderings of the new p-cooling system.................................................................................... 27 

Figure 3.3 New “Capacitive” p-cooling system installed for testing with a moving web in the 

30 MW production equipment..................................................................................................... 27 

Figure 3.4 Waveforms from Spire: dashed line: light pulse; thin lines are current and thick lines 

are voltage waveforms for measurements ................................................................................. 29 

Figure 3.5 Spire waveforms for reduced light intensity using Mike Walter’s screens.................................. 30 

Figure 3.6 PMAX and VOC measurements made with a steady state source and with the Pulsed-Spire ...... 30 

Figure 3.7 QA cell [10 cm2

] I-V measurements taken at a range of sweep-rates with an insolation 

of 560 W/m2 ................................................................................................................................ 31 

Figure 3.8 QA cell [10 cm2

] I-V measurements taken at a range of sweep-rates with an intensity 

of 100 W/m2 ................................................................................................................................ 31 

iv
Figure 3.9 Cell voltage time-response as the cell current is stepped from 0 to IMP [left], and from 0 

to close to ISC [right] .................................................................................................................... 32 

Figure 3.10 Cell shunt resistance and Spire PMAX as a function of brush current ......................................... 33 

Figure 3.11 Radius of reduced voltage as a function of the radius of the shorted area for low current 

densities (0.5% AM 1.5).............................................................................................................. 34 

Figure 3.12 Raw RSHEET measurements as a function of raw RSHUNT measurements for non-etched 

and non-passivated samples for two different measurement geometries.................................. 34 

Figure 3.13 I-V curves for a 1” (left) and 2” (right) diameter area of a 14” wide cell illuminated 60% 

of AM 1.5 intensity ...................................................................................................................... 35 

Figure 3.14 Schematic of proposed technique to eliminate the effect of shunts surrounding a small 

illuminated measurement area on a cell prior to etching and passivation.................................. 35 

Figure 3.15 Measurements from bench-test in-situ I-V measurements system using “guard electrode” 

and a Spire measurement of a completed cell ........................................................................... 36 

Figure 3.16 ITO I-V system brush holder....................................................................................................... 37 

Figure 3.17 Mounting system for brush holder .............................................................................................. 37 

Figure 3.18 Measured slab power ................................................................................................................. 37 

Figure 3.19 Measured slab shunt resistance................................................................................................. 37 

Figure 3.20 Raw data for top-cell charging rate............................................................................................. 38 

Figure 3.21 The AM 1.5 spectrum, lamination transmission, quantum efficiency of the triple junction 

product ........................................................................................................................................ 39 

Figure 3.22 Plot showing intensity vs. position for Blue LED’s at a fixed distance from the array ................ 40 

Figure 3.23 PVCD electronics chassis – Left: development chassis used in the first 25 MW 

production plant .......................................................................................................................... 40 

Figure 4.1 Measured PMAX as a function of Defect Density ......................................................................... 42 

Figure 4.2 Numerically calculated affected area as a function of defect radius .......................................... 43 

Figure 4.3 Experimental procedure for testing and evaluating cells and modules as a function of 

defect density.............................................................................................................................. 45 

Figure 4.4 PMAX as a function of Cell Line defect measurement level ......................................................... 46 

Figure 4.5 PMAX as a function of defect density for different levels of insolation.......................................... 47 

Figure 4.6 Initial PMAX as a function of defect density at 15% of AM 1.5 for all cells used in this study ...... 47 

Figure 4.7 Change in RShunt, Dark after HF and TC......................................................................................... 48 

Figure 4.8 Change in PMax, 15% after HF and TC .......................................................................................... 48 

Figure 4.9 Change in PMax,AM 1.5 after HF and TC........................................................................................ 48 

Figure 4.10 Change in PAM 1.5 after Light Soaking......................................................................................... 49 

Figure 4.11 Change in PMax, 15% after Light Soaking...................................................................................... 49 

Figure 4.12 Outdoor relative power output vs. insolation level for the average of the 3 modules in 

each bin ...................................................................................................................................... 50 

Figure 4.13 Outdoor relative power/insolation vs. insolation level for the average of the 3 modules in 

each bin after 1 year outdoor exposure normalized to the average of the Bin A modules......... 50 

Figure 4.14 Same data as in Figure 4, but with normalization to the Bin A modules .................................... 51 

List of Tables 

Table 3.I Principal advantages of online and offline diagnostic systems. ................................................. 28 

Table 3.II Summary of solar cell settling times vs. measurement accuracy for the two current steps 

shown in Figure 10...................................................................................................................... 32 

Table 3.III Comparison of Light Source Possibilities for the online ITO I-V System.................................... 38 

Table 3.IV Scenarios for balancing currents at the AM 1.5 light level in the triple junction device 

using our existing LED Light Source ......................................................................................... 39 

v
vi
BACKGROUND AND EXECUTIVE SUMMARY

Short Summary of Accomplishments 

In this program ECD has developed a comprehensive in-situ diagnostic system that: 

• Reduces the time between deposition in the a-Si machine and device characterization 

from about 200 hrs to about 1 hour. These systems allow us to immediately detect 

significant production problems and also pin-point these problems to specific deposition 

zones, consequently saving up to a week of production per incident. These systems 

have also provided us with the capability of embarking upon a significant machine 

optimization program that will increase the device efficiency, and consequently product 

rating, and reduce product variations. 

• The Photovoltaic Capacitive Diagnostic [PVCD] systems measure the open circuit 

voltage [VOC] and Charging Rate [CR, a measurement of the short circuit current (ISC) 

and intra-cell series resistance (RS)] for each cell in the triple junction device prior to 

deposition of the top conductive oxide coating in a subsequent deposition machine. 

These systems operate with an rms precision of about 0.03%, and have operated for 

close to 4 years with no need for servicing of the electronics or for calibration. We 

believe that these systems, which operate with a non-phosphor solid state LED light 

source, have demonstrated our capability of installing high-precision diagnostic devices 

to expedite the commissioning of manufacturing equipment, and then operating these 

diagnostic devices for the lifetime of the equipment without service or calibration. 

• Spectrometers are used to measure the ZnO thickness of the backreflector [BR], the 

a-Si thickness, and top conductive oxide, ITO, coatings. Closed loop control systems 

have been developed for both the ZnO and ITO deposition systems. A “Stop-and-Go 

On-the-Fly Static Burn” system allows us to measure the deposition rate and deposition 

profile of each of the more than 60 a-Si alloy deposition zones, including the very thin pand n- doping layers. 

With these systems a single individual will be able to easily monitor the operation of 10’s of 

machines on a real-time basis; in the further future, expert systems will be able to monitor and 

automatically optimize 100’s of machines using these diagnostic systems. 

In this report we summarize the diagnostic systems developed in this program, as well as our 

other major accomplishments. This report concentrates on work carried out in the final [3rd] 

phase of this program, beginning in the Fall of 2004 and ending in the fall of 2006. This 

information in this report is mostly a subset of the information we have already summarized in 

our Monthly Reports. A summary of the previous 2 phases of this work can be found at the 

URL: 

http://www.nrel.gov/docs/fy05osti/37660.pdf. 

Note: Many of the figures in this report show “screen displays” taken from diagnostic 

system computer displays. Color is used extensively in these displays to convey 

information; consequently, some information will be lost in a black and white copy 

of this report. We encourage you to view the color PDF file of this report that will 

be made available on the NREL website. 

vii
In particular, displays show Bottom Cell data in shades of red, Middle Cell data 

in shades of green, and Top Cell data in shades of blue – this allows one to 

more easily understand plots with many curves. 

Background 

Energy Conversion Devices, Inc. (ECD) has developed and built 7 generations of roll-to-roll 

amorphous silicon PV production equipment. In the ECD/United Solar Ovonic production 

process we deposit an ≈ 1 μm-thick 12-layer coating consisting of a metal/oxide backreflector, 

a 9 layer a-Si/a-SiGe alloy triple junction solar cell, and a top transparent conductive oxide 

coating onto 125 μm thick, 35.5 cm wide stainless steel webs in a series of three roll-to-roll 

deposition machines. Figure 1 shows a schematic of the United Solar 30 MW 6-web a-Si 

deposition machine; a photograph of the machine is shown in Fig. 2. N1

I1

P1

N2

I2

P2

N3

I3

P3

Gas

Gate

6 Pay-Off Chambers

6 Rolls of Stainless

2.6 km long x 36 cm

wide x 0.13 mm thick

6 Take-Up Chambers

6 Rolls of Stainless

Steel Subtrate

Coated with < 1 um

thick thin film triple

junction photovoltaics

Al/ZnO

Stainless

Steel

Ovonic a-Si Triple Junction Solar Cell Processor

Top View

Side View

N1

I1

P1

N2

I2

P2

N3

I3

P3

N1

I1

P1

N2

I2

P2

N3

I3

P3

Fig. 1. Schematic of the United Solar 30 MW/yr a-Si deposition equipment. 

All the developments of the previous PVMaT 5A program were incorporated into this machine: 

• A substrate heating and monitoring system, using durable NiChrome heater elements; 

• Reactive sputtering for low-cost deposition of the Al/ZnO backreflector; 

viii
• A new PECVD cathode that provides uniform deposition over large areas and reduced 

germane usage; 

• “Pinch Valves” that allow the rolls of substrate to be installed and removed while 

keeping the deposition regions of the machine under vacuum; and 

• Hardware for online diagnostic systems, including the non-contacting PV Capacitive 

Diagnostic (PVCD) system, which can measure the a-Si solar cell electrical properties 

in-situ without an ITO top coating, and spectrometers to measure the cell thickness. 

“Included in the machine” might be an understatement: many of these technologies have been 

essential to the success of this machine. This machine is now serving as basis to allow us to 

rapidly expand our manufacturing operations, as recently announced, to: 

 60 MW 2006 

 120 MW 2007 

 180 MW 2008 

 > 300 MW 2010 

 Fig. 2. Side view of the United Solar 30 MW/yr a Si processor. 

In this PV Manufacturing R&D Inline Diagnostics Solicitation, ECD has built upon these 

accomplishments to enhance the operation of the present production machine, lay the 

foundation for improvements in the next generation machines and has created the technology 

needed to operate 10’s, and perhaps 100’s, of machines. The four major work areas 

undertaken by ECD and United Solar in the present program are described below: 

1. Development of closed loop thickness control systems for the ZnO, ITO layers, and a-Si 

component layer thicknesses (the last is an expansion of this program). 

 2. Continued development of the PV Capacitive Diagnostic (PVCD), in particular, the 

development of new “Component Cell” PVCD’s for precise measurement of each of the 

3 cells in the triple junction PV device. 

ix
 3. The development of plasma monitoring systems to further optimize the i-layer 

deposition process, and for possible online implementation. 

 4. Yield improvements by investigating online yield measurement systems, investigations 

into sources of reduced yield, and mitigation of these sources. 

The PV Manufacturing R&D Program has played a key roll in the incubation and development 

of this Roll-to-Roll manufacturing technology allowing ECD/United Solar to develop new 

technologies to enhance the present equipment and lay the foundation for our now rapid 

expansion in manufacturing capability. 

1986: 0.6 MW

1991: 2.0 MW 1996: 5 MW

2007: 120 MW

2006: 60 MW

2003: 25 MW

0

50

100

150

200

250

300

1985 1990 1995 2000 2005 2010

Year

Capacity [MW/yr]

2008: 180 MW

2010: 300 MW

Fig. 3. ECD/United Solar Joint Venture manufacturing capacity over the last 15 years and 

announced plans for future expansion. 

ECD has now completed the Phase III work for this program. In the following report we 

summarize the work in each of these tasks. 

x
1. DEVELOPMENT OF CLOSED-LOOP CONTROL OF FILM THICKNESS

Jeff Karn (ECD), Tim Ellison, Rob Kopf, Mark Lycette (ECD); 

Rujiang Liu (United Solar); 

David Dodge (Focus Software) 

1.1 Introduction 

We have successfully met, and in many areas, surpassed the goals for this work area: 

• Backreflector (BR) Machine: We have implemented and now operate the ZnO 

deposition under closed loop control using a single thickness measurement system. 

• ITO Machine: We have developed and implemented a much more complicated MIMO 

(Multiple Input/Multiple Output) system for control of the thickness and cross-web 

uniformity of the ITO deposition. This system will probably replace the single 

spectrometer system presently used for ZnO thickness control in future backreflector, 

BR, machines for more transversely-uniform ZnO deposition. 

• a-Si machine: Using both the spectrometer system and the PVCD’s, we developed and 

used a number of QA/QC techniques. The discovery and development of the “Stopand-Go” static burn technique allows us to monitor the deposition rate and profile of 

each of the more than 60 deposition areas in the a-Si machine. With the present new 

process chamber spectrometer being developed, we see the possibility of having an 

expert system precisely track the deposition rate and profile for each cathode – 

including the very thin p-doping layers – in future machines for the ultimate in online 

thickness control. 

Our diagnostic systems have shown us that additional diagnostic systems to monitor and 

control other properties of the deposited thin films (e.g., the BR texture and reflectance, and 

the p3-ITO tunnel junction voltage drop) can further enhance our ability improve the product 

efficiency and reduce variations. 

1.2 3rd Generation ITO/ZnO CLTC Systems 

The original operational plan for the Backreflector (BR) and ITO Deposition Machines called for 

measuring the film thickness at a single location along the middle web. This real-time 

measurement would provide the operators with the diagnostic required for tuning the machine. 

Further, this diagnostic would become the sensor for an automated closed loop thickness 

control (CLTC) system. This plan has been successfully implemented on the BR Machine 

where a single thickness measurement along the center web was adequate. The BR CLTC 

system has now been in operation for two years and has significantly minimized the need for 

operator intervention. 

During commissioning of the ITO Machine it was found that transverse uniformity variations 

required an array of five spectrometers. In addition to measuring thickness, the oxygen 

distribution lines were outfitted with control valves to permit transverse thickness adjustment. 

Shown in Fig. 1.1 is the spectrometer operator display that is used for manually tuning the 

thickness. The top plot in this figure shows the instantaneous thickness of each of the five 

spectrometers while the bottom plot graphs these values over time. The step changes in the 

bottom plot occurred when the operator changed system settings to bring the thickness into 

specification. 

1
The layout of the ITO Machine spectrometers and control points are shown schematically in 

Fig. 1.2. There are two Plasma Emission Monitors (PEMs) that control valves distributing 

oxygen to front and rear manifolds. In addition, each manifold has two valves for adjusting the 

cross-manifold distribution. 

Fig. 1.1. ITO Spectrometer Operator Display. Top plot shows instantaneous thickness of 

all five spectrometers while the bottom plot graphs these five values over time.

FRONT WEB MIDDLE WEB REAR WEB

F1 F2 R1 R2

PEM-F

R1 F1 F2 M R2

PEM-R

Oxygen Valves

Plasma Emmission Monitor

Thickness Monitors

Fig. 1.2. Schematic Layout of ITO CLTC Inputs and Outputs

2
1.2.1 Closed Loop Thickness Control 

Due to the multiple spectrometers and controls the development of the ITO CLTC system was 

significantly more complicated than the BR system. The ITO CLTC algorithm breaks the 

control process into three subsystems: 

• Front Web Subsystem: Adjusts the front web uniformity by controlling the oxygen 

valves F1 and F2. Adjusts the overall front web thickness by changing the set-point of 

Plasma Emission Monitor PEM-F. 

• Rear Web Subsystem: Adjusts the rear web uniformity by controlling the oxygen valves 

R1 and R2. Adjusts the overall rear web thickness by changing the set-point of Plasma 

Emission Monitor PEM-R. 

• Middle Web Subsystem: No separate knob exists to adjust the middle web. Rather, the 

middle web thickness is controlled by coordinating the Front Web & Rear Web 

Subsystems. 

Tuning each subsystem involves quantifying how changes to the inputs (valves and PEMs) 

correspond to the measured change in thickness. It was found that a single proportionality 

constant could be used for the Front and Rear subsystems to control the oxygen valves. 

Shown in Fig. 1.3 is the CLTC Expert Display. The proportionality constants for the oxygen 

valves and the PEM control are shown inside the red boxes of Fig. 1.3. 

Fig. 1.3. CLTC Expert Display. Red boxes highlight the proportionality constants for the 

oxygen valves and PEMs.

3
1.2.2 Production Operation 

Upon determining the proportionality constants the system was run in closed loop mode for 

over 600 meters of a production run. The wavelength of the ITO reflection minimum plotted 

over the run is shown in Fig. 1.4. Between 200 and 1400 meters, when CLTC was not 

running, large drifts and the occasional step-changes implemented by the operator can be 

seen. Between 1400 and 2100 meters the system was operating in closed loop mode. During 

this part of the run the drifts disappeared and the deviations between spectrometers 

significantly decreased. [It should be noted that the thickness in the middle continued to be an 

outlier. This is a limitation imposed by the gas distribution system.] 

540

550

560

570

580

590

600

200 400 600 800 1000 1200 1400 1600 1800 2000 2200

Meter Mark

Wavelength of ITO Minimum (nm)

Front Outside (F1)

Front Inside (F2)

Middle (M)

Rear Outside (R2)

Rear Inside (R1)

OPEN 

LOOP

CLOSED 

LOOP

Fig. 1.4. Wavelength of the ITO Reflectance Minimum over a production run. The 

CLTC system began closed-loop mode at the 1400 meter mark. The resulting 

improvement to stability and uniformity can be seen.

1.2.3 Further Evaluation and Future Plans 

While proof-of-principle has been demonstrated, full time operation of this system has been put 

on hold to evaluate hardware and procedural changes to the gas distribution system. These 

changes will require re-tuning the subsystems and determining new proportionality constants. 

The control software changes to accommodate the new gas manifold have been completed 

and are under test. The full time operation of the ITO CLTC system will be implemented soon. 

4
1.3 a-Si Thickness and Process Control 

1.3.1 Introduction 

When we began this program we knew that is was important to control the thickness of the 3 

cells in our triple junction device in order to maintain and optimize the device conversion 

efficiency, but we had little hard data documenting the degree of device thickness regulation. 

With the PVCD in the previous [5 MW] production machine, we were able to observe and 

document, online, top cell Charging Rate [CR] changes of ± 10%, as shown in Fig. 1.5. These 

changes in the top cell Charging Rate [CR] affected the product conversion efficiency, and 

were composed of ≈ 15% step changes corresponding to a single cathode plasma turning ON 

or OFF, and slower drifts of about the same magnitude, probably due to the build-up of a-Si 

flakes on the cathodes. 

Fig. 1.5 Top Cell Charging Rate signal from the PVCD in the 5 MW production machine 

recorded over a 25 day period in 2002. One can observe a cathode going OFF 

and back ON [white boxes], as well as slow drift. The use of offline QA/QC, 

with its 1 -2 run delay to adjust the top cell thickness, resulted in one or several 

runs with a significantly non-conforming top cell thickness.

1.3.2 Long-Term Measurements in the 25 MW Equipment 

The goal of this program was to implement a system to measure and control, online, the 

thickness of each cell in the triple junction device. We developed two tools to measure the 

individual cell thicknesses: the Component Cell PV Capacitive Diagnostics (PVCD’s) Charging 

Rate [CR] signals and the Component Cell Reflection Spectrometers. 

5
Fig. 1.6. Data Diagnostic Display (DDD) covering 250,000 m of operation showing a slow 

decrease in PV device charging rate signal from the PVCD [top plot, 5%/div] and 

correlated decrease in the relative thickness of the PV material from the Bottom, 

Bottom + Middle, and Bottom + Middle + Top cell reflection spectrometers [bottom 

plot, 1%/div]. Some other events causing signal variations are noted. [Horizontal: 

250,000 m full scale].

I3A1 OFF I2F2 OFF 

Figure 1.6 shows a record of these systems in operation in the 1st 30 MW production 

equipment over a 1⁄4 million meter operating period. The upper plot shows a “composite” 

PVCD Charging Rate, CR, signal (with information from all three cells), and the lower plot 

shows the relative thickness of the Bottom, Bottom + Middle and Bottom + Middle + Top

cells. At the beginning of 2005, these data were telling us quite clearly that: 

• The device was becoming thinner, having lost about 5% of its thickness. We knew this 

with high certainty since independent measurements were telling us the same story; 

• The reduction in thickness was occurring in all cells; and 

• From previous correlations with offline QA/QC, the reduction in thickness was reaching 

a critical point where efficiency would be affected. 

We were most concerned about the top cell thickness, and performed an experiment in which 

we increased the top cell thickness as shown in Fig. 1.7. This experiment showed that the 

device efficiency increased as we adjusted the top cell thickness back towards its nominal 

value using the online diagnostic systems for feedback.

6
I3 Thickness 

Experiment

Further Cathode 

Cleanage

Partial Cathode 

Cleanage

 Spire PMAX



 PVCD Top Cell 

CR

Fig. 1.7. Online and Offline data showing effect of using the online PVCD top cell CR 

signal to correct the cell thickness. We observed about a 5% increase in 

device conversion efficiency when the top cell thickness was increased.

We then performed a high resolution Static Burn, Fig. 1.8, to see whether we could learn more 

about the source of the cell thinning problem. 

Fig. 1.8. Static Burns prior to cathode cleaning [top], after initial cleaning [middle], and 

standard Golden Profile [bottom].

7
We learned that the deposition profiles were low and distorted as shown in Fig. 1.8 (top). The 

Operations Group hypothesized that this may be due to a build-up of deposition on the 

cathodes and cleaned the cathodes. This resulted in the improved deposition profile seen in 

Fig. 1.8 (middle). 

Figure 1.9 shows thickness and charging rate data leading up to the cathode cleanage, and for 

about 100 subsequent runs during which a Preventive Maintenance procedure was instituted 

by the USO Operations group to eliminate large deposition build-ups on the cathodes. We see 

that during this period the top cell charging rate stayed within about a 5% band, and then 

decreased by 5% when the web speed was increased by 5%. We clearly have an excellent 

diagnostic system with extremely good long-term stability, to monitor and stabilize the top cell 

thickness. 

Fig. 1.9. Upper Plot: Top Cell Charging Rage, Offline Spire ISC, and Top Cell Quantum Efficiency 

Measurements; Lower Plot: Reflection Spectrometer Complete Device Thickness, and 

PVCD Complete Device Charging Rate.

Cathode 

Cleanage 

5% Web Speed 

Increase 

I1F2 

OFF 

I3A1 

OFF 

1.3.3 Short-Term Quality Control (QC) using the PVCD CR Measurement 

The thickness measuring devices have also been used for online Quality Control (QC), as 

demonstrated in Fig. 1.10. 

8
Fig. 1.10. Upper Plot: PVCD Bottom, Bottom + Middle, and Bottom + Middle + Top relative VOC, and 

Offline Spire VOC () and PMAX (). 

Middle Plot: PVCD Middle, Top, and Complete Device Charging Rates 

Bottom Plot: Reflection spectrometer Bottom + Middle, and Complete Device relative 

thickness. 

In the Orange Boxed region, a cathode off in the top cell intrinsic region caused a reduction in 

the top cell charging rate, and the device thickness. At the Green Vertical Bar, a correction 

was made to compensate for the missing cathode; using feedback from the PVCD, a second 

correction was made at the Red Vertical Bar. In the top plot one can see the effect this 

correction had on the device efficiency (PMAX).

1.3.4 Conclusion and Future Work 

We have demonstrated – over the course of a number of years now – the ability to measure 

the charging rate and thickness of the PV device with good precision and a high degree of 

long-term stability. The systems have been used both to correct for long-term drift in the 

deposition conditions, and online for short-term QC to correct for deposition problems. 

9
Our work in the future is focused in three areas: 

• Increased reliability to eliminate the need for servicing (e.g. changing spectrometer light 

bulbs); 

• Expanding our vision from thickness control to process control. Our present online 

systems show us that that there are variations in our product quality coming from 

process that are presently not under closed loop control, e.g. BR texture and reflectivity, 

and p3-ITO tunnel junction voltage drop; and 

• Improving our static burn profilometry by the use of process chamber spectrometers to 

remove the baseline noise in the profiles. This will allow an expert system to precisely 

and quantitatively track the deposition rate and profile for each cathode in the a-Si 

machine, including, we think, even the very thin low p-doping regions. 

1.3.4.a Increased reliability 

We are carrying out further work to improve the long-term stability of the web position in the 

regions where we make spectrometer measurements to reduce the frequency of alignment 

and calibration. We shall also be looking at replacing the incandescent bulbs in these 

spectrometers with LED sources which should last the lifetime of the production machine. 

1.3.4.b Additional process control 

The online diagnostic systems have identified a number of variations in the process, affecting 

the product conversion efficiency, that are unrelated to the film thicknesses we are controlling, 

e.g.: 

• The backreflector texture and reflectivity; and 

• The p3-ITO tunnel junction voltage drop 

1.3.4.c Improved Static Burn Signal-to-Noise 

We perform two types of on-the-fly static burns: 

• A high resolution burn, as shown in Fig. 1.8, where the web is stopped for about 3 

minutes, and then restarted. This long burn results in a very good S/N profile to identify 

a serious production problem; the burn also creates material unusable for product. 

• A standard burn in which the web is stopped for only 20 s. This puts a relatively small 

amount of additional deposition in the stack which contains 3600 s of deposition; 

consequently the product in not affected and no scrap is produced; on the other hand, 

the deposition profile is much noisier. We can use these noisier data to confirm that 

cathodes are operating, but we cannot not use these profiles for precise quantitative 

measurements; nor can these profiles resolve the thin p-layer depositions. 

The S/N for the short standard burns could be much improved by making differential 

measurements between adjacent process-chamber spectrometers – the measured baseline 

noise from an upstream spectrometer can be subtracted from a downstream spectrometer 

measuring the baseline and added deposition from the short burn (Fig. 1.11). 

10
Fig. 1.11. Top Plot: circled region shows effect of 20 s static burn on spectrometer 

signals – the magnitude of the deviation is about 1%. 

Bottom Plot: Spectrometer signals measured after the I1, P2, and N2 

layers are deposited. Once can how the “noise” is mainly due to real 

thickness changes from previously deposited layers. 

Key to making this work will be a very stable thickness measurement made inside a process 

chamber. To accomplish this we have designed, and fabricated, a spectrometer design 

(Fig. 1.12) which significantly reduces the distance between the spectrometer optics and the 

web, the single most important optical consideration, and incorporated a new web-stabilization 

scheme. As with the significant improvements developed for the next generation Component 

Cell PVCD’s described in the next section, this improvement was made possible by designing 

the spectrometer in synergy with the the vacuum chamber to optimize the performance of the 

diagnostic system hardware. 

With these differential measurements we believe we shall be able to use an Expert System to 

precisely and quantitatively track the deposition thickness and profile for each of the 60 + 

cathodes in the a-Si deposition machine – the ultimate in online QA/QC. 

11
Fig. 1.12. New Mark Lycette design for in-situ process chamber spectrometer. Top: installation 

showing how the device is angled to position the optics as close as possible to the web; 

Middle: inside alignment fixture; Bottom: close up the business ends: left: alignment 

adjustment mechanism; right: final remote optics adjustments. The design geometry 

eliminates reflections from the window.

12
2. COMPONENT CELL PV CAPACITIVE DIAGNOSTIC (PVCD) DESIGN AND FABRICATION 

FOR BOTTOM AND MIDDLE CELLS

 – Tim Ellison, Rob Kopf, Jeff Karn and Wayne Messing (ECD) 

Dave Dodge (Focus Software) 

2.1 Introduction and Background 

In the PVMaT 5A program we developed the PV Capacitive Diagnostic (PVCD) [1]. This 

device has proven its capability to make precise (≈ 0.05%) in-line measurements [2,3] of the 

PV device open circuit voltage, VOC, and short circuit current divided by cell capacity, JSC/C. 

The PVCD also provides information on the cell series resistance, RS. This non-contacting 

device works on material both before and after application of the top ITO coating, and has 

become an essential online diagnostic for production QA. 

The goal of this program was to extend this technology to be able to characterize each

component cell of the triple-junction device. While the original PVCD could precisely measure 

the triple-junction device VOC, it could not unambiguously tell us which of the three cells had 

changed. We envisioned a series of three PVCD’s, one following each completed cell. 

The original PVCD was developed to operate in the Take-Up chamber of the a-Si machine. 

This task was “simple” in that in the Take-Up chamber: 

• There are few space constraints; 

• The device is far from any EMI-generating plasmas and heaters; 

• The device can easily be shielded from extraneous light and ionized gas generated by 

plasmas; 

• The device design did not require the use of ultra-high purity vacuum materials and 

techniques necessary to install the device inside the deposition part of the machine. 

• The device was easily accessible for service, modifications, and calibration.

The Component Cell PVCD developed in this program had all these additional constraints – 

and it was not clear whether it could even be made to operate in such an environment. 

The Component Cell PVCD’s are now operating for both the Bottom and Middle cells as 

precision online Quality Assurance (QA) tools that allow us to measure the open circuit 

voltage, VOC, and charging rate, CR, online, for each cell in the triple junction device. 

 

[1] Tim Ellison, “Non-contacting PV Capacitive Diagnostic (PVCD) System for real-time in-situ 

analysis, QA/QC, and optimization”, Proc. 28th IEEE Photovoltaic Specialists Conference

(Anchorage Alaska, 15 – 22 Sept 2000).

[2] Greg DeMaggio et al., “Development of Online Diagnostic Systems for Roll-to-Roll a-Si Production: 

ECD’s PV Manufacturing R&D Program”, Proceedings of the 2003 NCPV Review Meeting 

(Denver, 24 – 26 March 2003). 

[3] Masat Izu and Tim Ellison, “Roll-to-roll manufacturing of amorphous silicon alloy solar cells with insitu cell performance diagnostics” in Solar Energy Materials and Solar Cells, Elsevier Science B.V., 

Amsterdam. 

13
The PV Capacitive Diagnostic (PVCD) measurement system has a number of features that 

enable it to assist and carry out optimization activities including: 

• Precision – the measured standard deviation for PVCD open circuit voltage, VOC and 

charging rate, CR, measurements is about 0.04% - 0.05%; consequently the PVCD 

system can resolve very small changes in device performance, undetectable in offline 

QA/QC, as process conditions are varied . 

• Spatial Resolution – the systems presently sample the web about every 1- 2 m – more 

than an order of magnitude higher spatial resolution than offline QA/QC measurements 

which enables a much higher throughput and bandwidth for continuous automated 

optimization. With a slight decrease in signal to noise we could increase the bandwidth 

of these devices to measure each cell (0.25 m resolution). 

• High Feedback Rate – the online PVCD systems decrease the delay between 

deposition and characterization by more than a factor of 100 – from on the order of a 

week to the order of an hour. 

• Long Term Stability and Reliability – To ensure reliability, the devices have built-in 

diagnostics and calibration systems: 

-- An online system measures the calibration of the entire electronics system every few 

minutes with a precision of 0.01%. 

-- There are also other system-diagnostics, such as a continuous measurement of the 

substrate temperature, data-logging of the LED light source current, and 

measurement of the light pulse amplitude. 

The PVCD systems have operated for over 3 years without need for calibration or 

replacement of any electronics. By contrast, a DC simulator must have its bulb 

changed on about a monthly basis which requiring recalibration, in addition to changes 

in calibration throughout the month. This exceptional degree of long term stability is a 

result of the stable geometry and the use of phosphor-less solid state (LED) light 

sources. We are now seeing the possibility of installing these systems to expedite 

commissioning and then operate for the lifetime of the machine without need for recalibration or major service – essential attributes for diagnostic systems as ECD 

expands United Solar Ovonic’s manufacturing capacity from a single machine today to 

about 10 machines by 2010, and perhaps 75 machines by 2015. We are also looking at 

expanding this LED light source capability to our spectrometer systems. 

These systems make online troubleshooting much easier – rather than saying we have a 

problem, we are now invariably able to quickly pinpoint problems to specific cathodes, online, 

and adjust the process online to compensate. Even in offline QA/QC testing, we only obtain a 

VOC for the complete device; online we now have precise information on the VOC for each of 

the component cells. 

14
The first generation Component Cell PVCD 

developed in this program, shown in Fig. 2.1, is 

still in operation in the first United Solar Ovonics 

(USO) 30 MW production equipment. Based 

upon experience with these first generation 

experimental systems, a second generation 

system, Fig. 2.2-3, has been designed and 

fabricated for the second and future 30 MW 

production machines. Mark Lycette envisioned 

and modeled a new “Atrium Geometry” PVCD 

with many advantages. The PVCD system was 

designed in harmony with a new larger diagnostic 

chamber. This synergistic design allowed him to 

incorporate many improvements and overcome 

numerous design challenges. Enhancements 

include: 

• Removal of all the electronics and delicate 

electrical feed-thru’s from vacuum; 

• Elimination of inside-the-vacuum water 

cooling and argon purging of the PVCD 

head and electronics (e.g. LED light 

source, photo-detectors, etc.); 

• Elimination of inside-the-vacuum water 

cooling for the pusher plate; Fig. 2.1. Close-up view section view of the 

PVCD sensor head, shutter, and 

transparent view of the “pusher 

plate”.

• Provisions to access the PVCD head and 

all electronics without breaking vacuum; 

• Increased serviceability. 

This design also minimizes, or eliminates, risk: 

the new system is all outside the vacuum – the PVCD head can be removed during operation, 

and a simple component-to-cover plate swap returns the configuration to an empty diagnostic 

chamber. 

15
Figs. 2.2-3. Side and top sections showing the new Mark Lycette “Atrium Design” Component 

Cell PVCD. 

In the following 3 sections we highlight a few examples of how the PVCD system can be used 

for online QA/QC by looking at the system from three different perspectives: 

• Past usage for optimization, and Quality Control (QC), and problem diagnosis; 

• Near term future uses for added QC; and 

• Longer term QC from the perspective of operating 10 to 100 production machines with 

small core QA/QC and optimization group.

16
2.2. Uses of the PVCD System for Online QA/QC 

2.2.1 Past Uses of the PVCD System for Quality Assurance and Quality Control [QA/QC] 

In the past few years we have used these experimental/developmental devices to: 

• Diagnose severe problems and stop runs for repair: This system has several times 

detected significant process problems, and pinpointed to source of the problem to a 

specific cathode in the 60+ cathode a-Si deposition machine. In each case, the run was 

stopped, avoiding long runs making scrap material, with the problem diagnosed. A 

single severe un-diagnosed production problem in a single a-Si deposition machine can 

cost United Solar 1 - 2 million dollars in lost sales. 

• Diagnose problems and correct problems on-the-fly during a run. We have also used 

the PVCD system to diagnose and correct problems on-the-fly. 

• Diagnose and correct long-term drift problems prior to their becoming critical. The longterm stability and high precision of the PVCD systems allow us to observe, and correct, 

long-term drift problems before they can be detected with offline QA/QC and before they 

become critical. 

• Optimize the process to increase the product efficiency. The high precision of the 

PVCD system allows us to detect small variations in product characteristics with small 

variations in process inputs, allowing us to optimize the process. Every percent 

increase in efficiency leads to about 1 M$/year/machine increased profits when 

incorporated into a product rating change. 

• Diagnose source of product variation. The PVCD system can detect small product 

variations due to small changes in the process, allowing us to determine the source of 

the variation. 

2.2.1.a Diagnosis of severe problems – “Saved Runs” 

The most easily documented cost savings result from the use of the online diagnostic systems 

to stop a 400 kW-batch run at the beginning, with a problem not only identified, but also 

diagnosed to a specific chamber or cathode. In such a situation we not only save the current 

run, but subsequent runs and the time that would be spent trying to diagnose the source of the 

problem offline. We have identified cathode outages in doping regions and water leaks.

Below we show an example of how the PVCD system recognized a critical process problem in 

the a-Si production machine and initiated a process stop after pinpointing the problem to a 

specific cathode. At the beginning of the run the PVCD open circuit voltages, VOC, were low 

for the Bottom + Middle and Bottom + Middle + Top Cell PVCD’s causing an alarm. Since 

there was no VOC problem detected by the Bottom Component Cell PVCD, the data clearly 

showed that the problem originated in the middle cell. A subsequent “Stop-and-Go” static burn 

using the spectrometer system pinpointed the problem to the n-2 cathode. The machine was 

stopped and the problem corrected. 

Perhaps one of the most important features of this Run Stoppage was the fact that at the time 

this problem occurred, the principal diagnostic system expert was out of town at NREL. The 

problem was correctly recognized and diagnosed by the production staff using the tools that 

we developed in this program. 

17
Fig. 2.4. DDD screen capture showing the effect of the n-2 cathode outage on the online open circuit 

voltage measurement made by the Bottom Component Cell PVCD, Bottom + Middle 

Component Cell PVCD, and Bottom + Middle + Top Component Cell PVCD. Also 

shown are the offline Spire VOC [] and PMAX [] measurements. The red rectangular box

shows where the production run was stopped and re-started. [Vertical: 10%/div; Horizontal: 

1000 m full scale]. 

2.2.1.b On-the-Fly problem detection and correction 

In section 1.3.3, in discussing closed loop thickness control in the a-Si machine, we showed 

and example of how the PVCD and spectrometer systems had been used to detect, pinpoint, 

and correct for a cathode outage in the I3 region without stopping a run. 

2.2.1.c Recognition and correction of long-term drift 

In section 1.3.2, in discussing closed loop thickness control in the a-Si machine, we showed 

and example of how the PVCD and spectrometer systems had been used to detect, pinpoint, 

and correct for a long-term drift problem in the I3 deposition rate. 

2.2.1.d Optimize the process to increase the product efficiency. 

The high precision of the PVCD system allows us to detect small variations in product 

characteristics with small variations in process inputs, allowing us to optimize the process. 

Every 1% percent increase in efficiency leads to about 1 M$/year/machine increased profits 

when incorporated into a product rating change. Below we show an example of how the 

PVCD system was used to identify a specific change in the process that lead to improved 

product conversion efficiency. 

Very small perturbation experiments, which were not observed in offline QA/QC, indicated that 

the device current and voltage could be increased by increasing the thickness of the n3 layer. 

Figure 1 shows subsequent experiments making large variations in the n3 layer and the 

corresponding increase in the product PMAX ([], top plot Fig. 1) as measured by offline QA/QC. 

At the beginning of the run we also see another example of an “on-the-fly” correction to correct 

the top cell charging rate that lead to about a 7% increase in product efficiency [Black vertical 

line, Fig. 2.5]. 

18
Fig. 2.5. DDD screen capture showing the on-the-fly correction of a PVCD-detected I3 cathode outage 

and the resulting improvement in product, and n3 thickness modulation experiments – we we 

a couple of percent increase in PMAX [] with increased n3 thickness. [Horizontal: 800 m full 

scale].

2.2.1.e Diagnose sources of product variations 

We provide a couple of examples that show how the PVCD system, combined with the 

Diagnostic Data Display, easily make clear the sources of variations in the product quality. We 

look at two examples: variations in the germane concentration of the Bottom and Middle cells, 

and variations in the reflectivity and texture of the Backreflector. 

Variations in the germane concentration

Germane concentration variation causes changes in the product electrical properties, 

principally the bottom cell current and VOC. The PVCD system clearly shows these production 

variations as shown in Fig. 2.6, where changes in the germane gas source are mapped on the 

bottom-most plot (circled in orange). 

19
 

Fig. 2.6. DDD display showing online and offline device measurements which are 

strongly affected by the change in Germane gas source. 

Top Plot: Bottom, Bottom + Middle, and Bottom + Middle + Top Cell VOC

as measured by the online PVCD system, and offline Spire VOC [] and PMAX

[] measurements [Vertical: 1%/div; Horizontal: 2000 m full scale]. We 

observe about a 1.5% change in the product VOC and PMAX when the 

germane gas bottle is changed. 

Middle Plot: Various Charging Rate signals from all three PVCD units; note in 

particular the plum-colored trace which shows the Charging Rate for the 

Bottom Cell when illuminated with an infrared light source [5%/div]. This signal 

was developed and is displayed to provide a highly-leveraged signal to monitor 

the device germane content. 

Backreflector Reflectance and Texture Variations 

Figure 2.7 clearly shows that nearly all the variations in the device VOC are a consequence of 

the changes in the VOC of the Bottom and Middle cells – and the vast majority of these 

variations originate in the Bottom cell as shown in Fig. 2.8. 

20
 

Fig. 2.7. DDD* display showing the United Solar product relative open circuit voltage, VOC, as 

measured by offline QA/QC [■] and online Bottom + Middle Component Cell PV Capacitive 

Diagnostic* [

] over twenty 2500 m runs [Veritcal: 1%/div; Horizontal: 60 km full scale]. This 

figure demonstrates the accuracy and precision of the PVCD device, as well as its ability to 

diagnose variations in product performance: from this plot we can see that nearly all 

variations occur prior to deposition of the top cell; additional diagnostics from the Bottom 

Component Cell PVCD allowed determination of the sources of these variations. The faint 

small orange points show the offline Spire PMAX measurements. 

Figure 2.8 shows is a screen capture of the online Data Diagnostics Display (DDD) showing 

measurements of the Online Bottom Cell VOC and the Offline Spire triple-junction VOC (■) and 

PMAX (■). The online bottom cell VOC measurements are from the Bottom Component Cell 

PVCD. Component VOC measurements are not available in any offline QA/QC measurements.

Fig. 2.8. Data Diagnostic Display showing the Online Bottom Cell VOC and Offline Spire complete 

device (triple-junction) VOC (■) and PMAX (■) measurements for 16 consecutive runs. 

[Vertical: 1%/div; Horizontal: 50,000 meters full scale].

This figure plainly shows that: 

• The Bottom Cell accounts for nearly all of the complete device VOC variations observed 

in offline QA/QC, including the ≈ 2.5% (± 1%) drop in complete device VOC from the 

beginning to the end of a coil. This 2.5% VOC drop corresponds to a very significant 

10% (± 4%) drop in the Bottom Cell VOC. 

• The drop in VOC is also associated with an ≈ 2 - 4% drop in PMAX. 

21
Other online data not shown in Fig. 2.8 (the Bottom Cell Charging Rate for a far infrared light 

source) allow us to conclude that the VOC drop is not caused by an increase in the percentage 

of germane in the bottom cell during a run. Figure 2.9 shows a screen capture of the online 

Data Diagnostics Display for two consecutive a-Si runs where the effect of the Backreflector 

can be clearly seen. 

• Upper Plot: Offline Spire VOC (■) and PMAX (■) and online PVCD Bottom Cell VOC (■) 

[1%/div]. The online Bottom Cell VOC data (■) are taken with more than an order of 

magnitude finer spatial resolution than offline data; the offline data are usually delayed by 

several runs as shown in Fig. 2.8. 

• Middle Plot: Two online Component Cell PVCD measurements of the Bottom Cell Charging 

Rate, strongly related to the cell short circuit current density, JSC [5%/div]. These data are 

from the Bottom Component Cell PVCD. The data plotted in purple is the result of 

measurements made with an infrared LED light to clearly show the effect of changes in 

bandgap (e.g. germane concentration) effects. The purple vertical line marks a germane 

bottle change which resulted in a higher percentage of germane being incorporated in the 

cell, which consequently decreased the Bottom Cell bandgap which resulted in an 

increased infrared charging rate and decreased VOC. 

• Bottom Plot: Two measurements of the backreflector specular reflection taken from the 

spectrometers in the Payoff chamber of the a-Si machine. Reductions in specular 

reflectance can be a consequence of (combinations of) increased texture and increased 

scattered reflection, increased ZnO absorption, or decreased aluminum reflection. 

 Fig. 2.9. Screen capture of online Data Diagnostics Display [6000 m FS 

• The decrease in PMAX [top plot] is highly correlated with decreased BR specular 

reflection [signal in bottom plot]. 

• The fact that the Bottom Cell IR Charging Rate signal in the middle plot 

decreases along with the Bottom Cell VOC [top plot] tells us that this is not 

related to a changing germanium/silicon ratio in the intrinsic region. 

These system variations have been mapped to specific process changes in the BR 

machine. 

22
2.2.2 Near-term future uses of the PVCD System for QA/QC 

Near Term Future Uses 

The performance of these experimental devices has exceeded our expectations. While we 

have already demonstrated many of their uses for online QA/QC, they are perhaps rather 

under utilized as merely an online Acceptable/Non Acceptable indicator. Other uses for the 

PVCD system we envision in near future include: 

• Germane content stabilization. In Section 2.2.1.d we illustrated the high degree of 

germane concentration control that could be provided by the Bottom Cell PVCD system. 

• Online cell matching and optimization. The PVCD system can expedite the 

commissioning of future machines by optimizing the cell matching and germane content 

optimization online in few hours. 

Figure 2.10 shows the measured changes in online measurement of the device charging rates 

and thickness in a series of experiments in which the web speed, and cell thicknesses, were 

varied by 5%. 

Fig. 2.10.. Online data from a run where there were 5% cell thickness and web speed changes. 

The online diagnostic systems have the precision to accurately see, and correct for, online, less than 

1% changes [1800 m full scale]. 

Top Plot: Online PV Cell Charging Rate for the Bottom, Middle, and Top cells measured by the 

three component cell PVCD’s in the AH-1 30 MW a-Si machine [5%/div]. 

Bottom: Online spectrometer thickness measurements for the Bottom, Bottom + Middle, and 

Bottom + Middle + Top cells [2%/div]. 

One can clearly see that the PVCD devices can easily resolve 1% changes in the thickness of 

each cell, and, the PVCD devices can maintain their charging rate calibration to the percent 

level for years. Consequently, we can envision sending a web from an existing production 

23
machine through a new machine being commissioned to calibrate the PVCD devices, and 

then, online in a few hours, adjusting the new machine to precisely fabricate the same product 

as the existing machine. Today this process involves multiple runs, with intervening ITO 

deposition and offline QA/QC. 

This same capability could also allow us to, online in a matter of hours, change the web speed 

and 

• Adjust all the cells back to within 1% of their nominal thicknesses; 

• Verify and adjust the bottom and middle cell bandgaps (Si/Ge ratio); and 

• Verify that the doping layers are thick enough and of good quality. 

2.2.3 Longer Term PVCD Uses 

As great as the past and near term advantages of the PVCD system are, we see the greatest 

payoff in the future when we shall be operating a large number of production machines. The 

PVCD system hardware may have already demonstrated the potential to be initially calibrated, 

used to expedite commissioning, and then quite literally, operate for the lifetime of the machine 

with no need for service of electronics or re-calibration. Such a high degree of stability will 

become essential as the number of production machines grows exponentially. Even more 

important as the number of machines grows exponentially, is a system that will provide 

automated online QA/QC, optimization, and detection and pinpoint diagnosis of production 

problems. These systems, with their exceptional signal-to-noise ratio, high degree of stability, 

and high spatial resolution, will have primary importance in such an Expert System. 

United Solar is in a phase of rapid expansion. Plans to expand production capacity to 300 MW 

(equivalent to 10 production machines) by 2010 have been announced. Continued growth at 

the industry average of about 40%/year would have us with the equivalent of about 100 

machines by 2016. One could easily imagine the nightmare, or manpower requirements, to 

support many hundreds of systems that need monthly or annual light source changes and 

recalibration. 

We believe that a fundamental requirement for diagnostic systems in such a vision is that they 

can be calibrated once prior to commissioning, and then operate for the lifetime of the machine 

without need of service or recalibration. We believe that we have perhaps already 

demonstrated this possibility. After 3 years of operation we have yet to see any measurable 

degradation of our solid state LED light sources. By reducing the repetition rate of the systems 

by order of magnitude, equivalent to the spatial resolution of the offline QA/QC measurements, 

we might expect these light sources to maintain this high degree of stability for 30 years, well 

beyond the expected lifetime of the production equipment. 

As these diagnostic systems are continuously improved, and when these systems become 

supported by Expert Systems, we believe it is quite reasonable to expect a few individuals to 

be able to easily support automated optimization and precise pinpoint diagnosis of production 

problems in hundreds of production machines. 

24
3. DEPOSITION OPTIMIZATION AND PLASMA DIAGNOSTICS

– Tim Ellison, Scot Jones, Tongyu Liu, Mark Lycette [ECD], Gautam Ganguli (United Solar)

3.1 Introduction and Summary 

Work has concentrated on two principle area: 

1. Investigation of the uses of plasma diagnostic systems to optimize the production 

process; and 

2. Improvements and optimization of the deposition process. 

3.2 Investigation of the Uses Of Plasma Diagnostic Systems to Optimize the 

Production Process 

We have experimented with many technologies to diagnose the deposition process, including: 

• Photo-detectors to monitor the light from plasmas; 

• Gas conductivity monitors; 

• Residual gas analyzers to analyze background contamination; 

• Residual gas analyzers to monitor plasma reaction products; 

• Spectrometers to monitor plasma reaction products 

For all these systems, the following blanket statements are more or less true: 

• Correlations can be found between plasma measurements and process conditions; 

• Much less clear correlations can be found between plasma diagnostic system 

measurements and the PV device characteristics; and 

• The correlations are usually valid for a specific working point; the calibration may vary 

significantly for different working points. 

Or, quite simply, in a production environment, one good measurement of a property of the 

deposited device is worth a hundred measurements of the plasma properties. In order to 

provide feedback for the deposition process, where the device properties are controlled to the 

percent level, only a good direct measurement of the device property will suffice. 

In this program we have developed a comprehensive system to measure the deposited device 

properties online, e.g.: 

• The PVCD systems which allow precision measurement of the open circuit voltage, VOC, 

and Charging Rate (CR α ISC/C) for each cell in the triple junction device, which also 

provides information on intra-cell series resistance; 

• The cell thickness and the deposition profile for each deposition region in the a-Si 

machine; 

• The thickness of the ZnO layer in the BR machine (under automated closed loop 

control), as well as the reflectivity and signals related to texture; 

• The thickness (under automated closed-loop control), color, and signal related to 

conductivity of the ITO. 

25
In the title of this research program is the word, “Comprehensive” – and so a good question to 

ask is, “In what way is our present diagnostic system not comprehensive”? And indeed, we he 

have two blind spots: 

1. Inter-cell series resistances (e.g. tunnel junction measurement). 

The PVCD system measures the device prior to 

application of ITO, charging each cell individually in a 

quintessentially open circuit condition; since there is no 

shunting from ITO, there is consequently no path to 

return current from the p3 layer to the n1 layer; absolutely 

no current flows between the p- and n-doped regions, or 

the tunnel junctions. Consequently we are almost blind 

to contamination in these areas [Fig. 3.1] 

Fig.3.1. Present blind spots: 

contamination at 

between the p and n 

(including ITO) layer.

Contamination in the very top of the p-layer, or the very 

bottom of the n-layer, such that the charging rate for the 

component cells is not affected but the tunnel junction is 

affected might seem, is at first glance, possible, though 

highly improbable. However, these regions are unique in 

that these are the only regions in the machine that we 

have installed water cooling, making the probability of a 

tunnel junction problem a whole lot higher!

In this program we have addressed this blind spot in 

perhaps the most important way, by removing the water 

cooling: 

• We designed and installed new “passive-cooling” systems to remove the water from the 

p-regions [Figs. 3.2–3]; and 

• We have also designed (and are now fabricating) a new “atrium design” Component 

Cell PVCD system [Figs. 2.2-3] which removes all the water and electrical feedthroughs from the system, and also provides better access. 

While we have consequently removed the proximate causes of inter-cell series resistances 

from the system, it may be years before our fears of such problems adjust to the new 

probabilities of leaks in these areas; and while the probability of contamination in these 

areas is now much reduced, to that of leaks in any other area of the machine, it is not zero. 

2. Desire for Complete Device Measurements 

The Component Cell measurement provide us with a detailed view of the cell and allow 

easy diagnostics of the source of problems and variations; however, we ultimately need a 

complete device measurement or we are still, in some senses, operating in an open-loop 

mode (Table 3.I). 

Al/ZnO

Stainless

Steel

N1

I1

P1

N2

I2

P2

N3

I3

P3

26
Fig. 3.2 Renderings of the new p-cooling system. 

[Side view, looking in the direction web 

transport direction, on the LHS, and view 

normal to the substrate surface on the 

RHS]. The aluminum plates close to the 

backside of the web [2] provide uniform 

substrate temperature. Heat is 

transferred to the door via the thin plates; 

shims [1] on these plates are used to 

adjust the temperature. This system also 

has provision to function as an rf 

grounding system; rf conductivity and 

thermal conductivity can be separately 

adjusted since the rf current flows only 

on the surface of the conductors. Also 

shown is the pre-heater and optional 

plate heater[3] to pre-heat the web as it 

enters the chamber and to adjust the 

deposition temperature to a greater 

extent online. By installing over-sized 

conductor shims, the temperature can be 

adjusted upwards using the second 

heater.

. 3

3

1

2

1

1

2

1

Fig. 3.3 New “Capacitive” p-cooling system 

installed for testing with a moving web 

in the 30 MW production equipment. 

The discoloration of the plates is from 

offline line testing in the “X” chamber 

at ECD’s Plant 2. 

27
Table 3.I. Principal advantages of online and offline diagnostic systems. The systems are 

complimentary.

Properties Online Diagnostics Offline QA/QC [Spire] Online System Advantages/Disadvantages

Feedback Period 1 - 2 Hours

An inline I-V measurement system, in combination with the present systems, provides us with 

all the advantages of both the inline and offline systems. We have consequently directed our 

efforts at designing and testing an in-situ I-V measurement system that will measure the 

complete device properties after application of ITO. 

1-2 Weeks

25 m - 1000 m

N/A Poor inter-cell effects characterization

X 100 feeback rate for QA, QC, and Optimization

Spatial Resolution 0.01 m - 2 m X 10 Increased experimentation rate 

Information Details of Individual Layers, Complete Device Only Pinpoint problems and process variations

Component Cell, and

Complete Device

Product Certification Good

3.3. Inline I-V Measurement System 

In this we summarize the development to date of this system which is now ready for installation 

and first testing in the 30 MW-I machine: 

3.3.1. Study of times needed to measure the thin cells 

 3.3.1.a. Pulsed mode; reduced intensity 

 3.3.1.b. Single sweep measurement 

3.3.2. Study of the effect of brushes contacting the ITO 

3.3.3 Study of the effect of the ITO sheet and shunt resistances on the non-etched non-passivated 

material 

3.3.4 Light source requirements, development, and testing 

3.3.5 Electronics 

3.3.6 Software 

3.3.1. Investigations of the Spire Measurement System and Time Needed to Make Accurate I-V

Measurements – in pulse mode, single-sweep mode, and at reduced intensities. 

3.3.1.a Pulsed Measurements; Investigations using the Spire System 

We have used the Spire I-V system to measure cell at reduced light levels in order to predict the 

performance of our product as a function of defect density as discussed in Section 4 [Yield 

Improvements]. We have had, however, some question about the validity of these measurements. We 

were curious about a couple (at least) things having to do with pulsed I-V measurements, e.g.: 

• How long does it take for our thin, relatively high (> 100 times, with respect to crystalline) 

capacitance cells take to charge up and reach equilibrium? 

• What about at reduced light intensities, where the charging time might be even an order of 

magnitude longer still? 

28
So, we made some measurements. In looking at these data we found that: 

• The 1 ms-long Spire pulse is not long enough to accurately measure the cell voltage for high 

load current in the manner in which it is being measured. 

• The inaccuracy increases and can clearly be seen when the light level is reduced with neutral 

density filters. 

• The trigger used in the present system is somewhat erratic – the light pulses and waveforms will 

jump back and forth on the scope when synched to the Spire data acquisition trigger. 

Observations 

AM 1.5 Measurements 

Figure 3.4 shows waveforms from the 

Spire I-V measurement system. See 

caption for explanation of signals. 

When the Spire flashes, we see the cell 

charge up to close to VOC in the first 

100 μs, before the light level reaches half 

its maximum value [red box]. At that point 

the current source pulses on [violet box]; 

the value to which the current initially goes 

is unknown – the op amp monitoring this 

signal saturates which gives the signals the 

flat cut-off level [dark red dotted line]. 

This pulls the cell voltage negative to about 

– 4 V. The current source then stabilizes 

at a constant value, and the cell begins to 

charge back up. When close to VOC, the 

cell does reach a stable value; however, 

for higher load currents, where there is less 

current for charging the cell, the voltage never stabilizes; it is still rising when the voltage is sampled 

(t = 0), peaks after the measurement, and then falls as the light level decreases. 

-15.0

-1 0.0

-5 .0

0.0

5 .0

1 0.0

15.0

-0.001 -0.0008 -0.0006 -0.0004 -0.0002 0 0.0002 0.0004 0.0006 0.0008 0.001

Time [s]

-1 .5

-1

-0.5

0

0.5

1

1 .5

Fig. 3.4 Waveforms from Spire: dashed line: light pulse; 

thin lines are current and thick lines are voltage 

waveforms for measurements close to shortcircuit current and close to open circuit (also 

voltage waveform for open circuit). 

• Voltage measurements are under-estimated as the cell load current is increased; 

• Voltage measurements might also be slightly over-estimated at VOC (load current = 0): one can 

see that for no load the cell voltage drops slowly compared to the light level; the Spire peakdetector circuit will remember the VOC measured at the higher light level prior to the 

measurement time. Of course this is a small (0.5 – 1% logarithmic) effect. 

Measurements at Reduced Light Levels – 

Since we are already seeing problems at AM 1.5, it’s not surprising that the problems are exacerbated 

at lower light levels. For lower than AM 1.5 levels of insolation, as Figs. 3.5 show, not only does the 

voltage often not reach a stable value by the point of measurement, but the peak is shifted to a point 

well after the time-of-measurement when the light level is falling. 

29
1.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

-0.001 -0.0005 0 0.0005 0.001

Time [s]

PV Voltage [V]

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

1.00

Photo Detector [Arb]

0.4

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

-0.001 -0.0005 0 0.0005 0.001

Time [s]

PV Voltage [V]

0.15

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

-0.001 -0.0005 0 0.0005 0.001

Time [s]

PV Voltage [V]

Figs 3.5. Spire waveforms for reduced light intensity using Mike Walter’s screens. The number at the top is the 

relative light level. 

0.08

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

-0.001 -0.0005 0 0.0005 0.001

Time [s]

PV Voltage [V]

These effects not only distort the I-V

curve at low voltage/high current 

conditions, but also at the maximum 

power point. Figure 3.6 compares VOC

and PMAX measurements made with a 

steady-state light source with pulsedSpire measurements. 

In the steady-state (Baojie) study, the 

PMAX and VOC both fall-off closely 

following a logarithmic curve; the Spiremeasured PMAX measurements, 

however, show a much more rapid falloff at reduced levels of insolation. 

0.60

0.70

0.80

0.90

1.00

1.10

0.0 0.2 0.4 0.6 0.8 1.0

Relative Insolation Level

Relative 

PMAX and 

VOC

Pmax -- Baojie

Voc -- Baojie

Pmax -- Spire from Mike

Pmax this study

Voc this study

Log. (Pmax -- Baojie)

Log. (Voc -- Baojie)

Fig. 3.6. PMAX and VOC measurements made with a steady state 

source and with the Pulsed-Spire. Relative PMAX is 

PMAX/JSC; relative VOC is VOC/VOC, AM 1.5. 

30
Just How Long of a Pulse Do We Need? 

We see cell voltage rise-times of as little as 70 μs for cells with no load and the Spire light source 

reaching only 50% of its maximum value and so expect we could see rise times of about 35 μs at 

AM 1.5 levels for no load. BUT – these measurements did not have the resolution to show whether we 

see longer-time-constant effects at the percent level. 

3.3.1.b Cell Measurement Times for Single-Sweep Measurements 

We continued our investigations of the length of time needed to obtain accurate I-V measurements for 

our thin-film high-capacitance solar cells. This is of interest to us for a couple of reasons: 

• We have documented that the pulsed Spire simulator reads low when measuring at reduced 

light intensities; and 

• we want to investigate the possibility of making a full I-V scan in a single light pulse. 

We build a current-controlled I-V sweep system and tested cells under the Oriel DC light source. As 

shown in Figs. 3.7-8, we begin to see significant distortion of the measured I-V curve for sweep times of 

less than about 3 ms. In these figures, the legend refers to the total sweep time; the time to sweep 

over the range of interest, however, is about 60% of this time. 

Fig. 3.7 QA cell [10 cm2] I-V measurements taken at a range of sweep-rates with an insolation of 560 W/m2. 

Left: sweep from JSC to VOC; Right: sweep from VOC to JSC. [The displayed current is 1/10th the actual 

current]. 

Fig. 3.8 QA cell [10 cm2] I-V measurements taken at a range of sweep-rates with an intensity of 100 W/m2. Left: 

sweep from JSC to VOC; Right: sweep from VOC to JSC. [The displayed current is 1/10th the actual current]. 

31
We also looked at the time response of the solar cell. In Table 3.II and Fig. 3.9 we show the time 

needed for the cell voltage to settle to various levels after a step change is made in the solar cell 

current. 

 

Fig.3.9. Cell voltage timeresponse as the cell current is stepped 

from 0 to IMP [left], and from 0 to close to 

ISC [right]. [100 μs/div (2 μs/data point)].

Table 3.II. Summary of solar cell settling times vs. 

measurement accuracy for the two current 

steps shown in Fig. 10.

Table II. Cell response times.

Measurement Settling Time [μs]

accuracy VOC to VMP VOC to JSC

10% 20 100

5% 40 150

2% 50 200

1.00% 80 300

0.50% 150 

0.20% 200 

Some of the tentative conclusions we have thus far include: 

• The cell equilibration times could be reduced by designing the V-I measurement circuit to 

specify the cell voltage, and have a current capability in excess of that of the solar cell – 

especially for discharging the cell. 

• There isn’t “one number”: the time depends upon the measurement being made, and how the 

measurement is performed. 

• A 5% accurate measurement might be very accurate if calibrated against a higher accuracy 

measurement and performed in the same way – for a product with fairly constant cell properties. 

3.3.2. Cell Measurement Techniques – Effect of Brushes on the Material 

We investigated of the use of brushes to make contacting I-V measurements inline in the ITO machine. 

Such a system, in combination with inline-ITO deposition, would reduce the delay in getting I-V

measurements by about a factor of 200. 

We have used brushes in the past for measurements of the shunt resistance and ITO sheet resistance 

– measurements which draw very little current, e.g. < 100 μA. Cell I-V measurements would require 

drawing a few more orders of magnitude of current through the brushes – something which we worried 

might damage the web. We consequently decided to perform experiments to determine how much 

current could be drawn though a brush before causing damage. 

The experiments were performed as follows: 

• On a series of cells, prior to passivation and etching, currents in the range of 1 μA to 100 mA 

were drawn through a single brush on the cell. The brush was moved across the cell at the 

same speed that the web moves in the ITO deposition machine. 

• The experiments were performed both with cells illuminated by a solar simulator, and with cells 

at very low light levels (in which case we reversed the polarity of the current to keep the PV 

diodes forward biased). 

• All the cells, along with two sets of controls (cells that were never touched by a brush, and cells 

touched by a brush but with no current) were then sent through the cell line. We looked for 

damage by: 

32
o Visual inspection (e.g. passivation marks or discoloration); 

o Measurement of the cell shunt resistance; and 

o Measurement of the cell PMAX at AM 1.5. 

We saw no visual evidence of cell damage. Electrical measurements, as shown in Fig. 3.10, also 

showed no correlation with brush current. 

R2 = 0.019 R2

 = 0.0583

R2

 = 4E-06

Rshunt -- Light

R2

 = 0.014

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

0.001 0.010 0.100 1.000 10.000 100.000

Brush Current [mA]

RSHUNT [arb]

7.0

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8.0

Spire 

PMAX [W]

Rshunt -- Dark

PMAX -- Light

PMAX -- Dark

Linear (PMAX -- Light)

Linear (PMAX -- Dark)

Linear (Rshunt -- Light)

Linear (Rshunt -- Dark)

Fig. 3.10 Cell shunt resistance and Spire PMAX as a function of brush current. The was no 

visual cell damage or variation in cell electrical properties correlated with the 

brush current. 

This was an exciting result: it shows that we can easily draw short circuit currents at AM 1.5 insolation 

levels from brushes without damaging the web – a necessary prerequisite for measuring I-V curves 

inline in the ITO machine. 

3.3.3 Sheet and Shunt Resistance Measurements and Affects on I-V Measurement 

Accurate online measurements of the ITO sheet resistance and device shunt resistance will be 

necessary to correct the I-V curves made in-situ after application of ITO but prior to the etching and 

passivation processes – processes that reduce the degree of shunts and eliminate shunts at the edges 

of the material. 

The shunt and sheet resistances are coupled together: 

• The shunt resistance is limited by the sheet resistance because of the high current densities at 

the point of shunting, as shown in Fig. 3.11. 

• The sheet resistance also is affected by the shunt resistance since multiple shunts provide 

parallel paths for the sheet current. This effect becomes more apparent, as might be expected, 

as the distance over which the sheet resistance is measured is increased, as shown in 

Fig. 3.12. 

Reliable online and de-coupled RSHEET and RSHUNT measurements will be necessary to correct the 

online I-V measurements. We are developing these measurement systems and will test them in the 

ITO deposition machine. 

33
 

Fig. 3.11Radius of reduced voltage as a function of the 

radius of the shorted area for low current 

densities (0.5% AM 1.5). As the radius of the 

shunted area is increased by a factor of 105

, 

from the thickness of the film to 1 mm, the 

radius of the reduced voltage area increases 

by a factor of 2 from about 4 to 8 cm. 

R2 = 0.281

R2 = 0.77

50

55

60

65

70

75

80

85

90

95

20 30 40 50 60

R SHUNT [Ohm]

RSHEET [Ohm/Square]

Rsheet vs Rshunt [1"]

Rsheet vs Rshunt [2"]

Fig. 3.12 Raw RSHEET measurements as a function of raw RSHUNT measurements 

for non-etched and non-passivated samples for two different 

measurement geometries. 

34
Measurement of I-V Characteristics of non-etched non-passivated PV 

We tested measurement of PV device I-V curves on PV material prior to etching and passivation to test 

the feasibility of full I-V measurements in the ITO machine. The I-V curves were trivial, as shown in 

Fig. 3.13 (left), in that the measurement described perfect resistors; the resistance being low enough 

that even at VOC, all the light-generated current passed through the shunts and the diodes in the PV 

devices never began conducting. 

Fig. 3.13. I-V curves for a 1” (left) and 2” (right) diameter area of a 14” wide cell illuminated 60% of 

AM 1.5 intensity. The cell has not been etched or passivated. 

6

The principle shorting mechanism is the combination of ITO sheet conductivity and shunting outside the 

measurement area, including the shorting at the edges of the non-etched cells. This is demonstrated 

by making the same measurement but on a larger area, as shown in Fig. 3.13 (right), since the ratio of 

the measurement area to the perimeter shunt increases in proportion to the diameter of the 

measurement area. 

We conjecture that the effect of the shunts surrounding the area being measured can be eliminated by 

using two sets of contact brushes as shown in Fig. 3.14: the inner set to measure the I-V curve of the 

illuminated area, and an outer set, powered by a separate power supply, to provide the necessary 

current to the shunts surrounding the measurement area. 

Fig. 3.14. Schematic of proposed technique to eliminate the effect of shunts 

surrounding a small illuminated measurement area on a cell prior 

to etching and passivation.

I-V Amp

Guard 

Grid Amp

35
We procured power supplies, wrote software, and built hardware to test this concept. The results, 

shown in Fig. 3.15, are promising; 

• A dark measurement provides an accurate measurement of the resistive shunt resistance, and 

this value can be used to remove the effect of these shunts from the I-V curve. At high light 

intensities in a passivated cell, the effective shunt resistance is due only to recombination 

current, so removing this resistance is justified. As shown in Fig. 3.15, this fix moves the test insitu I-V curve into agreement with an AM 1.5 measurement at the ISC end of the curve (blue 

curve, Fig. 3.15). 

• The “grids” in the test in-situ I-V are about twice the distance apart as in the completed cell 

measured in the Spire, increasing the series resistance. Adding a small series resistance 

correction (orange curve, Fig. 3.15) brings the slope of the test in-situ I-V curve into agreement 

with the offline Spire curve. The in-situ system will also perform ITO sheet resistance 

measurements to also allow real-time correction of the I-V curve. 

• After both these corrections, we still see the test in-situ I-V curve VOC below the AM 1.5 Spire 

measurement. Most of this shift is due to the logarithmic dependence of VOC on light intensity 

and the lower light intensity and increased shadowing used in the test fixture. 

On the basis of these results we moved ahead to finish engineering of the first generation system for 

testing in the 25 MW production equipment.

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0 0 0.5 1 1 .5 2 2 .5

-6

-5

-4

-3

-2

-1

0

Grid -- Uncorrected

Grid -- Rsh Corr

Grid -- Rsh, Rs Corr

Spire [Cell 0600]

Voltage

Current

I SC

V OC

0

Fig. 3.15 Measurements from bench-test in-situ I-V measurements system 

using “guard electrode” and a Spire measurement of a completed 

cell. See text for explanation. 

Unlike the PVCD’s, the I-V system needs to make contact with the surface of the web to draw current 

from the device. A key to making this work is a set of contacts that can draw current from the web 

without damaging the web and minimizing the amount of shadowing. We worked with a brush 

manufacturer that makes conductive brushes for static applications to develop brushes for our 

application. We then designed a brush holder and mounting system for these brushes, as shown in 

Figs. 3.16-17. 

36
 

Fig. 3.16 ITO I-V system brush holder. Fig. 3.17. Mounting system for brush holder. 

We also tested these brushes for web-damaging effects. In our tests, as shown in Figs. 3.18-19, we 

observed no statistically-significant difference in the electrical properties of control cells, cells that made 

contact with the brushes, and cells that made contact with the brushes and had much higher current 

densities in the brushes than cells will have in normal operation. 

7.66

7.68

7.70

7.72

7.74

7.76

7.78

7.80

7.82

7.84

4030 4040 4050 4060 4070 4080 4090

Slab

PMAX [W]

Control

Brush 

Brush & Current

4030 4040 4050 4060 4070 4080 4090

Slab

Shunt Resistance [arb]

Control

Brush 

Brush & Current

Fig. 3.18. Measured slab power. Fig. 3.19. Measured slab shunt resistance. 

3.3.4. LED Light Source Development/Extreme Reliability 

ECD has recently announced that United Solar Ovonic will have a manufacturing capacity of 300 MW 

by 2010 – equivalent to 10 production machines. Assuming USO continues to expand at the industry 

rate, the number will approximately double every 2 years. In this coming regime we shall literally have 

on the order of 100 online diagnostic systems with their number continuing to grow exponentially. We 

see the need, and the possibility as demonstrated by the performance of the PVCD systems, to design 

systems that do not require routine maintenance, service, or even calibration; literally, systems that will 

be installed to expedite the commissioning process and continue to operate, without need for service or 

calibration, for the lifetime of the manufacturing equipment, always providing the same reading for 

product with the same characteristics. 

37
Figure 3.20 provides a glimpse of this degree of reliability. It is a record of the top cell charging rate, 

CR, measured over the last 3 years [about 400 runs, or 0.8 million meters of web] from the a-Si TU 

PVCD. This signal has maintained its integrity, accuracy, and precision over this period without 

calibration or service [other than twice annual dusting]. 

Fig. 3.20. Raw data for top-cell charging rate, CR, over last 400 runs [Vertical: 

2%/div; Horizontal: 800 km FS]. A few operating events affecting the CR 

measurement are shown.

Cathode 

Cleanage

5% Web Speed 

Increase 

Cathode 

Outage 

This high degree of reliability is made possible by the use of a solid state LED light source which, if 

operated within limits, could enable operation without need for maintenance or calibration for more than 

10 years. Table 3.III shows a comparison of possible light sources available for use in the ITO I-V

measurement system. 

Table 3.III. Comparison of Light Source Possibilities for the online ITO I-V System. 

Pulsed DC LED

I-V Measurements

 Accuracy Suspect

Harder

Good Good

 Ease Easy Easy

Lifetime Year? 10 - 100 yr [Est]

Spectrum

 Measurement Easy Easy

 Time Dependence

1 month

Difficult

During Aging Aging Aging?

Can be corrected

 Class 

During Pulse

A Y Y N, but can match I

Availability

 Small Area N Y Y

 Long Pulse N Y

 Horizontal Config ?? Not Std, Class B? Y

 Lead Time Couple Weeks

Un-Reliability Highly Probable

n/a 10 - 12 Weeks?

n/a Doubtful

The LED light source developed by ECD would be our choice for such highly reliable systems. There is 

only one drawback: diodes with the wavelengths and intensities necessary to build a Class A solar 

simulator are not yet commercially available; the principle problem is the relative low intensity of the 

diodes in the amber/green band, as shown in Fig. 3.21. (For the highly reliable systems we envision, 

we are, for now, not considering the use of phosphor-LED’s, since the phosphors can age with 

use/time). 

38
0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

300 400 500 600 700 800 900 1000

Wavelength [nm]

0

1 000

2 000

3 000

4 000

5 000

6 000

7 000

2mil Tefzel 8 000

2mil T/GMC+26mil EVA / 2mil T

ASTM AM 1.5 Global + Tilt

Q Top

Q Mid

Q Bot

Q Tot

Class A

New Blue LED

New Amber LED

New Red LED

New IR LED

I [mA]

100.0

100.0

100.0

100.0

Desire 7.0 7.0 mA/cm2

Erro

7.0

r 43.1% 2.3% -25.8%

Fig. 3.21 The AM 1.5 spectrum, lamination transmission, quantum efficiency of the triple junction 

product, Class A wavelength and power density requirements, and spectral radiometer 

measurements of the ECD PVCD Blue, Amber, Red and Infrared LED solid state 

light source for 100 mA current in each diode string. 

We can, however, ask a different question: rather than asking whether we can build a Class A 

simulator, which allows a ± 25% power density error in each of the six wavelength bins, we could ask 

the following question: can we build a solid state light source that matches the current in each of the 

component cells of our triple junction device at AM 1.5 levels? The answer to that question appears to 

be YES. As summarized in Table 3.IV: 

• Using just two LED colors (Blue and Red) we can match the current within all three cells to 

within 2.5% [Scenario 2]. 

• Using 3 LED colors (Blue, Amber and Red), there is a unique set of LED currents that will 

exactly match the current within all three cells [Scenario 3]; and as shown in Scenarios 4 – 5, 

• Using 4 LED colors (Blue, Amber, Red, and Infrared) there exist an infinite number of solutions 

to match the current in all three cells [e.g. scenarios 4 – 5]. 

• 

Table 3.IV. Scenarios for balancing currents at the AM 1.5 light level in the triple junction 

device using our existing LED Light Source. 

Scenario Current in LED String [mA] Current Error In Cells [Target: 7 mA/cm2

]

Blue Amber Red Infrared Top Middle Bottom

1 100 100 100 100 43% 2% -26%

2 55 0 161 0 1% -2% 2%

3 54 38 152 0 0% 0% 0%

4 53 80 137 100 0% 0% 0%

5 50 200 93 382 0% 0% 0%

39
Optical Measurements of the intensity vs. 

position for each of the 4 colored LED’s in 

the LED array were completed. A small 

subset of these data are shown in Fig. 3.22. 

The data provide a 3-D map: intensity as a 

function of x-y position, on a 0.5 inch grid, 

as a function of distance (z position) from 

the detector for each of the LED diode 

families. These measurements will help us 

dead-reckon the relative intensities of the 

different LED colors to provide the same 

current-matching in the triple junction 

device as does the AM 1.5 spectrum. 

Fig. 3.22. Plot showing intensity vs. position for Blue LED’s 

at a fixed distance from the array. The Blue Box 

shows the area of our first prototype I-V brush 

system.

3.3.5. Electronics Chassis 

The ECD Machine Division Electronics Department professionally packaged of the PVCD electronics 

for the 25 MW II facility, as shown in Fig. 3.2.23. Due to historical data-acquisition limitations, in the 

past we were limited to operating all LED colors with the same current. The first of these new chassis 

off the assembly line was modified to be used for the testing of the post-ITO-deposition in-situ V-I

measurement system under development. The electronics were modified to allow independent control 

of each color of LED; this will allow us to adjust the spectrum to cause the same current in each cell of 

the triple junction device as there would be when the device is illuminated by the AM 1.5 spectrum. 

Fig. 3.23. PVCD electronics chassis – Left: development chassis used in the first 25 MW production plant; Right: 

new professionally-packaged PVCD electronics chassis developed for the 2nd 25 MW facility. 

This feature will also allow us to: 

• Set the light levels for the different wavelengths so as to provide the same current in each cell 

as would an AM 1.5 spectrum. There are an infinite number of combinations of the 4 LED 

wavelengths that can be used; we plan to use of two or three of these combinations for a 

consistency check. 

40
• Online, measure efficiency as a function of insolation level; 

• The light source can be instantly varied from AM 1.5 to AM 0, or to simulate the effects of 

absorption in the later-applied lamination material; and 

• The light source spectrum can be varied to simulate, online, the effect of increasing or 

decreasing the thickness of each intrinsic layer in the triple junction device. 

3.3.6. Software 

We completed initial specifications and have written and tested software for the I-V measurement 

system software. We see 3 possibilities: 

1. Single Point: “Spire-Like” system: single data-point/light pulse 

2. Full Curve: full I-V curve in single longer light pulse 

3. Hybrid (e.g., a few long pulses to get full I-V curve, with “smart” ramp 

The Single Point (1st) Possibility will definitely work and is straightforward. The Full Curve (2nd) 

Possibility is much more preferable: 

• More data (e.g., cell-by-cell characterization); 

• Much (1 - 2 order of magnitude) longer LED lifetime. 

The Full Curve (2nd) Possibility will take much more development – many things will be close to their 

limits (e.g., solar cell time constant, LED pulse lengths, power supply and monitoring electronics 

bandwidths); furthermore, the LED output will vary within pulse since we do not yet have light feedback. 

We shall begin with Possibility #1. We shall use this straightforward technique to “get on the air”, and 

then in parallel develop Possibility #2. We shall be able to use the results of Possibility #1 to test/check 

the accuracy of Possibility #2. 

41
4. YIELD IMPROVEMENTS

Jon Call, Mike Walters [USO]; Tim Ellison, Rob Kopf [ECD]

4.1 SUMMARY 

In this work we: 

• Identified the largest source of yield loss in production: defects. 

• Chose an online measurement criterion to quantify degree of defects.

• Correlated defect level measurement to:

o Initial and stabilized cell and module performance;

o Cell performance after accelerated testing; and

o Cell and module performance as a function of insolation level using both solar 

simulator and long term outdoor testing.

• Recommend a new cell-line criterion to USO management; the criterion was accepted. 

• Carried out a long-term outdoor exposure test to look at the effect of defect density in 

cells over time. 

The results of this work, carried out by the USO QA/QC department and ECD was a new cell 

line measurement criterion for defect density and a significant (5 – 10%) increase yield and 

(10 – 15%) increase in throughput. While most of this work was completed in the Spring and 

Summer of 2005, outdoor testing has been, and is, continuing. We are also continuing our 

investigations of the effect of defects on cell performance for low light conditions, and the 

accuracy of our measurements at low light conditions using a pulsed simulator.

4.2. BACKGROUND 

Most, but not all, defects are removed in our proprietary passivation process; however, a few 

defects may remain. 

In the cell line we 

quantify the degree of 

defects and sort the 

cells accordingly. As 

shown in Fig. 4.1, at 

STC we observe no 

effect on the module 

PMAX as a function of 

defect density, even 

for the highest 

degrees of defects 

encountered in 

production. 

L-strip Pmax vs. Final VLL for CC#1900 to CC#2027

y = 0.0632x + 7.2365

R2

 = 0.0074

0

1

2

3

4

5

6

7

8

0.0 0.2 0.4 0.6 0.8 1.0 1.2

VLL (V)

PMAX (W)

Pmax [W]

Model: Homogeneous

Linear (Pmax [W])

Inverse Defect Density [1 = 0 defects; 0 = ∞ defects] 

 PMAX vs Inverse of Defect Density 

Fig. 4.1. Measured PMAX as a function of Defect Density. The gold line is a 

linear fit to measured data; the dark orange line is a model that 

creates a similar shunt resistance uniformly across the cell. 

Discreet defects behave fundamentally different. 

42
As also observed in Fig. 1, few discreet defects affect the cell differently than continuous 

homogenous defects. We believe that is due to the fact that the influence area of a shunt 

decreases as a function of light intensity on the cell. As shown in Fig. 4.2, the calculated 

influence area for a defect creating a dead short between the ITO and Backreflector is 

relatively independent of the size of the defect, and decreases approximately proportionally to 

the light intensity as a consequence of the ITO resistance. 

0.1

1

10

100

1000

1E-06 1E-05 1E-04 1E-03 1E-02 1E-01 1E+00

Defect Radius [cm]

Affected Area [cm2

]

AM 1.5

1% AM 1.5

 Fig. 4.2. Numerically calculated affected area as a function of defect radius. 

Consequently, in principle, a few shunts should not be able to affect the power output of our 

800+ cm2

 cell, though the effect might be seen at lower levels of insolation when affected area 

increases. We confirmed this result using the Spire simulator, and also investigated whether 

the degree of defect density affected the results of light soaking and accelerated tested. To 

further confirm our results, for perhaps even a single defect might somehow affect the cell 

quality over time, we also performed outdoor testing. 

4.3 Cell Sorting: 

We began work by collecting samples over a few months of operation and sorting them into 5 

“bins” with varying degrees of defects. This extended collection period was necessary for two 

reasons: 

1. We wanted to ensure that the samples in each bin were “typical”: although we have 

identified the primary source of defects, defect density levels variations might arise from 

many source and be affected by many operational variations. We did not want to select 

43
for a particular type or source of defect, or fill bins with an atypical or peculiar type of 

defect. 

2. We wanted to test material well outside of both the present and the projected 

acceptance levels of defect density in order to ensure that the criterion level was in a 

regime where the cell characteristics did not change rapidly with the defect 

measurement (i.e., we did not want to set the acceptance level close to a “cliff”). This 

range of bins would also allow us to make predictions about yield and performance as a 

function of criterion level. We consequently needed to look at a large number of 

samples to find enough “outliers” to fill the extreme bin ranges. Even after an extended 

period, we were unsuccessful in filling the extreme bin ranges and so performed a 

special production run to deliberately make a small amount of higher defect-density 

material for these tests. 

In the course of creating these Bins, measuring the samples, and designing the experimental 

procedure, we changed the number of Bins to 3: this allowed us to have better statistics and 

reduce the number of samples needed to send through accelerated testing and outdoor 

testing. The number of bins was arbitrary and merely a tool for initial selection of samples: 

each bin was populated with a range of defect levels and the data can later be plotted 

continuously or a function of new arbitrary Bins. 

The experimental procedure for this work is summarized in Fig. 4.3. 

4.4 Summary of Results of Indoor Studies 

We performed Thermal Cycling (TC) and Humidity Freeze (HF) measurements and 800 hour 

Light Soak (LS) tests of cells with a range of defect densities. Cells (124 L-slabs) were 

grouped into three Bins. We describe the tests and summarize the measurements showing a 

statistically-significant correlation with defect density level prior to, and after, accelerated 

testing. We found that: 

• There were no “catastrophic” reductions in PMAX at AM 1.5 that could be attributed to 

even the highest defect density levels studied. 

• We confirmed previous measurements showing that there is no significant dependence 

of PMAX upon defect density level before accelerated testing; in these tests we found 

that there is also no such dependence after accelerated testing.

• There is, however, a statistically-significant dependence of PMAX upon defect density 

level at reduced insolation levels. The initial dependence increases with accelerated 

testing performed with Forward Bias, or with Light Soaking. 

• We don’t know whether this reduction in PMAX at reduced light levels is related to 

Staebler-Wronski degradation; if so, we have identified a very small component in this 

catch-all phenomenon; if not, we have no certainty that this effect is stabilized after 800 

hours; on the other hand, it appears to be ameliorated by high temperatures. 

• We recommended new cell line measurement criterion that would increase cell line yield 

by 4% to 8%. The new criterion was adopted by USO.

44
Measurements: 

Dark; 1 Sun; 0.15 Sun 

Measurements: 

Dark; 1 Sun; 0.15 Sun 

Measurements: 

Dark; 1 Sun; 0.15 Sun 

Measurements: 

Dark; 1 Sun; 0.15 Sun 

Measurements: 

Dark; 

1 Sun; 

0.15 Sun 

Generate 3 Bins 108 

Samples per Bin 

Fig. 4.3. Experimental procedure for testing and evaluating cells and modules as a function of defect 

density. 

45
4.4.1 Initial Measurements (prior to accelerated testing) – Principal Findings 

Effect of Defect Density on Cell Power (Initial Measurements) 

At AM 1.5 there was no statistically significant correlation between PMAX and defect density as 

shown in Fig. 4.4, consistent with previous findings.

1 Sun P MAX

y = 0.0002x + 7.3463

R2

 = 0.0259

6.0

6.2

6.4

6.6

6.8

7.0

7.2

7.4

7.6

7.8

8.0

600 800 1000 1200 1400 1600 1800

Cell Line Initial VLL

Initial 1 Sun 

PMAX (W)

Inverse of defect density 

 Fig. 4.4. PMAX as a function of Cell Line defect measurement level. 

There was, however, a significant correlation between PMAX at lower light levels and defect 

density as shown in Figs. 4.5-6. The cells were measured in the Spire and neutral density 

filters used to attenuate the light level; the average of the JSC measurement for all the cells was 

used as a measurement of the light intensity and to normalize PMAX. The first-order effect is a 

reduction in normalized power with reduced light intensity independent of defect density level; 

the second-order effect is a further reduction with increasing defect density. 

At the time we made these measurements we had some questions about accuracy of the 

pulsed-Spire measurements at reduced light intensity due to the increased charging-time of 

the cell at reduced levels of insolation. We have since looked at this in more detail and now 

know that our suspicions were correct and that the pulsed Spire measurements exaggerate (or 

create) the fall-off of PMAX with reduced levels of insolation. This is discussed in section 

3.2.1.a. 

46
R2

 = 0.65

R2

 = 0.36

R2

 = 0.14

R2

 = 0.25

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

0.8 0.9 1.3 1.4

V LL (V)

Normalized 

PMAX

0.15 Sun P MAX

y = 1E-04x + 0.8976

R2

 = 0.2209

0.6

0.7

0.8

0.9

1.0

1.1

1.2

600 800 1000 1200 1400 1600 1800

Cell Line Initial V LL

Initial 0.15 sun Pmax (W)

 Fig. 4.5. PMAX as a function of defect density for different levels of insolation. 

 Fig. 4.6 Initial PMAX as a function of defect density at 15% of AM 1.5 for all 

cells used in this study. 

4.4.2 Final Measurements (after accelerated testing) – Principal Findings 

Humidity-Freeze (HF) and Thermal Cycling (TC)

• With no bias, the dark RSHUNT of the cells improved, though more so for HF than for TC 

(Fig. 4.7). After HF, all dependence of PMAX, AM 1.5 and PMAX, 15% upon initial defect 

density measurement disappeared; for TC there was no significant change (Figs. 4.8-9). 

1.0 1.1 1.2

1000 655

262 157

67

Insolation Level 

(W/m2

)

Inverse of defect density 

Inverse of defect density

47
The HF tests spend about 10 X as long at high temperatures, and consequently we 

expect more annealing. 

• With reverse bias, there were no statistically-significant trends. 

• With forward bias, thermal cycling increased the reduction in PMAX, 15% with increasing 

defect density (ANOVA p-value of 0.003).

Fig. 4.7. Change in RShunt, Dark after HF and TC. 

Fig. 4.8. Change in PMax, 15% after HF and TC. 

Fig. 4.9. Change in PMax,AM 1.5 after HF and TC. 

Dark Rsh

0.0

0.1

1.0

10.0

600 800 1000 1200 1400

Cell Line Initial VLL

1600 1800

after HF / initial 

50HF no bias

50HF rev bias

40HF fwd bias

Dark Rsh

R2

 = 0.4029

0.0

0.1

1.0

10.0

600 800 1000 1200 1400 1600 1800

Cell Line Initial VLL

after 200TC / initial

200TC no bias

200TC rev bias

200TC fwd bias

Linear (200TC fwd bias)

1 sun Pmax

R2

 = 0.0413

R2 = 0.026

4.00

4.50

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

600 800 1000 1200 1400

Cell Line Initial VLL

1600 1800

Pmax after HF

50HF no bias

50HF rev bias

50HF fwd bias

Linear (50HF fwd bias)

Linear (50HF no bias)

1 sun Pmax

R2 = 0.0569

R2 = 0.1867

4.50

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

600 1600 1800

Pmax after TC

800 1000 1200 1400

Cell Line Initial VLL

200TC no bias

200TC rev bias

200TC fwd bias

Linear (200TC fwd bias)

Linear (200TC no bias)

0.15 sun Pmax

y = -0.0001x + 1.1442

R2

 = 0.3242

y = 0.0001x + 0.7845

R2

 = 0.1741 y = -0.0002x + 1.0633

R2

 = 0.1507

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

600 800 1000 1200 1400

Cell Line Initial VLL

1600 1800

after HF / initial

50HF no bias

50HF rev bias

50HF fwd bias

Linear (50HF no bias)

Linear (50HF fwd bias)

Linear (50HF rev bias)

0.15 sun Pmax

y = 0.0002x + 0.6619

R2

 = 0.8021

0.60

0.70

0.80

0.90

1.00

1.10

1.20

1.30

600 1600 1800

after 200TC / initial

800 1000 1200 1400

Cell Line Initial VLL

200TC no bias

200TC rev bias

200TC fwd bias

Linear (200TC no bias)

Linear (200TC fwd bias)

Linear (200TC rev bias) Inverse of initial defect density Inverse of initial defect density 

Inverse of initial defect density 

Inverse of initial defect density Inverse of initial defect density 

48
4.4.3 Light Soak Tests 

At AM 1.5 the cells degraded by about 12%, with no correlation with initial defect density as 

shown in Fig. 4.10. 

1 sun Pmax

0.60

0.70

0.80

0.90

1.00

1.10

600 800 1000 1200 1400 1600

Cell Line Initial VLL

1800

after lightsoak / initial

 Fig. 4.10. Change in PAM 1.5 after Light Soaking. 

At 15% of AM 1.5 light intensity, the dependence upon PMAX with defect density increased as it 

did with the forward-biased TC test samples (Fig. 4.11). 

0.15 sun Pmax

y = 7E-05x + 0.8204

R2

 = 0.3631

0.60

0.70

0.80

0.90

1.00

1.10

600 8

Fig. 4.11. Change in PMax, 15% after Light Soaking.

The reduction in PMAX as a function of light intensity and initial defect density measurement 

were used in a model that fit the reduction in PMAX to defect density measurement and light 

intensity. We then used 5-minute period data over a 3 month period from the optical 

pyrometer on the NAHB Townhouse to calculate the effect on integrated power. We found a 

very slight reduction in integrated power for increasing defect density, though we suspect the 

pulsed Spire measurement overestimates the drop in normalized device efficiency with 

decreasing insolation level. 

00 1000 1200 1400

Cell Line Initial VLL

1600 1800

after lightsoak / initial

0.15 sun Pmax -- elliminated low point

y = 5E-05x + 0.8502

R2

 = 0.5351

0.60

0.70

0.80

0.90

1.00

1.10

600 80 1600 1800

after lightsoak / initial

0 1000 1200 1400

Cell Line Initial VLL Inverse of initial defect density Inverse of initial defect density 

Inverse of initial defect density 

49
4.5 Results of Outdoor Testing 

Figure 4.12 shows the relative measured power (PMAX/Insolationout/constant or relative 

efficiency) of these modules 

4 days after installation in 

the spring of 2005. We see 

the normalized power 

[Power/Insolation] first 

increase as the insolation 

decreases due to the series 

resistance in the circuit, and 

then decrease for smaller 

values of insolation. This 

decrease is clearly related to 

the defect density and 

modules with higher defect 

levels are more affected as 

the insolation level is 

decreased. 

0.0

0.2

0.4

0.6

0.8

1.0

0 20 40 60 80 100

Relative Insolation Level [AM1.5 = 100]

Normalized Pmax (W)

A

B

C

Fig. 4.12. Outdoor relative power output vs. insolation level for the 

average of the 3 modules in each bin. The modules were 

measured 4 days after installation. Bin A has the lowest 

defect density [standard], and Bin C has the largest defect 

density. 

What was of greatest interest to us was how this pattern changed after a year of outdoor 

exposure. The perhaps surprising results are shown in Fig. 4.13 where the module power has 

been normalized to the power of the modules in Bin A. We see that after time, the effects 

associated with defects on the module power output have decreased. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100

Relative Intensity [AM 1.5 = 100]

Normalized 

PMAX (W)

B-initial

B 1 year

C-initial

C 1 year

Fig. 4.13 Outdoor relative power/insolation vs. insolation level for 

the average of the 3 modules in each bin after 1 year 

outdoor exposure normalized to the average of the Bin A 

modules. 

50
Since the data in Fig. 4.14 that show an improvement after exposure are relative to the Bin A 

data, a question that immediately comes to mind is whether the Bin B and Bin C modules 

improved, or the Bin A modules degraded. In Fig. 5 we have removed the normalization to the 

Bin A modules. We observe a roughly 15% reduction in PMAX after stabilization (roughly inline 

with what we expect for a springtime measurement prior to annealing), and see that the effect 

of the defects at low light levels on PMAX has indeed decreased. 

0.0

0.2

0.4

0.6

0.8

1.0

10 30 50 70 90 110

Normalized Intensity [AM 1.5 = 100]

Normalized 

PMAX 

A - 1 Year

B - 1 Year

C - 1 Year

A

B

C

Fig. 4.14. Same data as in Fig. 4, but with normalization to the 

Bin A modules. 

Future Work 

These data need more careful analysis. 

We shall continue to monitor these modules over time to see whether this trend continues, or 

stabilizes. 

We previously used our indoor measurements of the reduction in PMAX as a function of light 

intensity and initial defect density measurement in a model using 5-minute period insolation 

data over a 3 month period from the optical pyrometer on the NAHB Townhouse to calculate 

the effect of the defect density on the integrated power. We found a very slight reduction in 

integrated power for increasing defect density, though we suspected that the pulsed Spire 

measurement overestimated the drop in normalized device efficiency with decreasing 

insolation level. We shall now use these data to update our model. 

These measurements provide more confirmation in our new cell line defect level sorting 

criterion. 

51
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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

May 2007 

2. REPORT TYPE

Subcontractor Report 

3. DATES COVERED (From - To)

23 April 2003 – 30 September 2006 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Implementation of a Comprehensive On-Line Closed-Loop 

Diagnostic System for Roll-to-Roll Amorphous Silicon Solar Cell 

Production: Final Subcontract Report, 23 April 2003 – 30 September 

2006 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/SR-520-41560 

5e. TASK NUMBER

PVB56101 

6. AUTHOR(S)

T. Ellison 

5f. WORK UNIT NUMBER

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Energy Conversion Devices, Inc. 

1621 Northwood 

Troy, Michigan 48084 

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NREL Technical Monitor: Richard Mitchell 

14. ABSTRACT (Maximum 200 Words): This report summarizes Energy Conversion Devices’ diagnostic systems that were 

developed in this program, as well as ECD’s other major accomplishments. This report concentrates on work carried out 

in the final (third) phase of this program, beginning in the fall of 2004 and ending in the fall of 2006. ECD has developed 

a comprehensive in-situ diagnostic system that: Reduces the time between deposition in the a-Si machine and device 

characterization from about 200 h to about 1 h; The Photovoltaic Capacitive Diagnostic systems measure the opencircuit voltage and charging rate (a measure of the short-circuit current) and intra-cell series resistance for each cell in 

the triple-junction device prior to deposition of the top conductive-oxide coating in a subsequent deposition machine. 

These systems operate with an rms precision of about 0.03% and have operated for almost 4 years with no need for 

servicing of the electronics or for calibration; Spectrometers are used to measure the ZnO thickness of the backreflector, 

a-Si thickness, and top conductive-oxide, coatings. 

15. SUBJECT TERMS: PV; on-line closed-loop diagnostic system; amorphous silicon; solar cells; photovoltaic capacitive 

diagnostic (PVCD); open-circuit voltage; charging rate; short-circuit current; top conductive oxide coating; backreflector; 

triple-junction device; 

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