NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Defect-Band Emission 

Photoluminescence Imaging on 

Multi-Crystalline Si Solar Cells

Preprint

Fei Yan, Steve Johnston, 

and Mowafak Al-Jassim

National Renewable Energy Laboratory

Katherine Zaunbrecher 

National Renewable Energy Laboratory and Colorado 

State University

Omar Sidelkheir and Alain Blosse 

Calisolar

Presented at the 37th IEEE Photovoltaic Specialists Conference 

(PVSC 37)

Seattle, Washington

June 19-24, 2011

Conference Paper

NREL/CP-5200-50725 

July 2011
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1 

DEFECT-BAND EMISSION PHOTOLUMINESCENCE IMAGING ON MULTICRYSTALLINE Si SOLAR CELLS

Fei Yan1, Steve Johnston1, Katherine Zaunbrecher1,2, Mowafak Al-Jassim1, Omar Sidelkheir3, and Alain Blosse3

1

National Renewable Energy Laboratory, Golden, CO 80401, U.S.A. 2

Colorado State University, Fort Collins, CO 80523, U.S.A. 3

Calisolar, Sunnyvale, CA 94085, U.S.A

ABSTRACT

Defect-band photoluminescence (PL) imaging with an 

InGaAs camera was applied to multicrystalline silicon (mcSi) wafers, which were taken from different heights of 

different Si bricks. Neighboring wafers were picked at six 

different processing steps, from as-cut to postmetallization. By using different cut-off filters, we were 

able to separate the band-to-band emission images from 

the defect-band emission images. On the defect-band 

emission images, the bright regions that originate from the 

grain boundaries and defect clusters were extracted from 

the PL images. The area fraction percentage of these 

regions at various processing stages shows a correlation 

with the final cell electrical parameters.

INTRODUCTION

Different imaging techniques have been developed 

recently and turned into rapid and nondestructive inline 

characterization tools. Electroluminescence (EL) imaging 

collects the light coming out from finished solar cells under 

forward bias [1-5]. Spatially resolved information about the 

recombination, resistance, and cracks can be obtained in 

seconds. Therefore, EL imaging has been widely used not 

only on finished cells, but also on assembled modules. 

Reverse-bias EL (ReBEL) imaging has been used to study 

the reverse breakdown behavior on different solar cells [6-

10]. However, because EL imaging requires electrical 

contacts, it is limited to use on the finished solar cells and 

any steps thereafter. PL imaging uses optical excitation to 

generate electron-hole pairs and a camera detects the 

radiative recombination emission [11, 12]. It has a great 

advantage as a contactless technique and can be applied 

to essentially all processing steps, from the silicon ingots 

and bricks to all processing steps on the wafer scale. A lot 

of work has been done on the band-to-band emission 

around 1150 nm using Si-CCD cameras, which gives 

great information about the defects and dislocation 

clusters inside the silicon wafers. Several papers have 

been published on mapping the defect-band luminescence 

at around 1550 nm [13-16], which corresponds to the 

factors which limit the final cell’s efficiency.

EXPERIMENT 

In this paper, we used an InGaAs camera to quickly image 

the defect-band emission and studied the defect-band PL 

images on 156 mm x 156 mm mc-Si wafers that had gone 

through six processing steps, including as-cut, texturing, 

emitter diffusion, phosphosilicate glass (PSG) removal, 

anti-reflection coatings (ARC) deposition and metallization. 

These wafers were selected from different bricks at 

different heights, including near the bottom, in the middle 

and near the top. We had 30 wafers from each step. We 

examined a total of 180 wafers, which give us enough 

data points for a statistical analysis.

We used a FLIR SC2500 NIR InGaAs Camera (320 x 256 

pixels) with a StingRay 25 mm wide angle lens for defectband PL imaging. The detection sensitivity of the InGaAs 

camera is in the 0.9 to 1.7 μm range, which covers both 

the band-to-band emission at ~1.1 eV and the defectrelated emission at ~0.8 eV from silicon, as shown in Fig. 

1. The optical excitation source used for PL imaging is 

composed of four 30 W, 810 nm laser diodes coupled to 

optical fibers. The fibers are then coupled to collimators 

and engineered diffusers. They emit from opposite angles 

to provide uniform excitation over the area of the sample. 

All PL imaging work is done at a condition close to 1 sun 

intensity. To block reflected light from the sample and 

stage area, RG1000 Schott glass filters are mounted to 

the camera lens. To separate the band-to-band emission 

and defect-related emission, different cut-off filters were 

mounted to the lens.

Figure 1 Quantum efficiency curve of the Near-IR 

InGaAs camera and the PL curve of a Si sample with 

both band-to-band PL peak and defect-band shown. 
2 

Figure 2 PL imaging of mc-Si solar cell comparing (a) 

band-to-band emissions to (b) defect-band emissions 

when using a 1350 nm long-pass filter.

Figure 3 Defect-band emission PL images on wafers 

after six different processing steps: (a) as-cut, (b) 

texturing, (c) emitter diffusion, (d) PSG removal, (e) 

ARC deposition, and (f) metallization. 

Fig. 2 shows a comparison of the band-to-band emission 

and defect-band-emission PL images on a mc-Si finished 

cell. Most of the regions that are dark in the band-to-band 

emission image Fig. 2(a) have sub-bandgap radiative 

emissions and are predominantly bright in the defect-band 

emission image Fig. 2(b). 

Defect-band emission PL images on six neighboring 

wafers from various processing steps are shown in Fig. 3. 

The same defect pattern persists from the beginning to the 

end. The as-cut and post-texturing wafers have a relatively 

weak PL signal due to the surface recombination, which 

can also be observed in the band-to-band PL images (not 

shown here). After emitter diffusion and PSG removal, 

wafers have a stronger PL signal but the contrast is not 

the best. After ARC and metallization steps, the defectband PL images have an excellent signal and contrast. 

One may notice that through these six processing steps, 

the relative intensity of some bright regions changes. For 

an example, near the top-left corner of Fig. 3(a) and (b), 

there is a big cluster with grey defect PL. Later after the 

following processing steps, this cluster becomes really 

bright with a very strong defect PL emission. The cause of 

this change is not totally understood at this moment. 

The size of the dislocation clusters grows along the 

direction of the solidification of the ingot, which is 

consistent with the band-to-band PL images we took on 

bricks (not shown here). Near the bottom of the brick, we 

observed a small fraction of the area with defect-band 

emission as shown in Fig. 4(a). The defect clusters 

become much bigger at the middle of the brick and almost 

dominates the whole wafer at the top of the brick. The 

efficiencies of these finished cells show a gradient 

decrease from the bottom to the top. This nice trend can 

be further quantified by counting the area fraction of the 

defect area over the whole wafer area.

Figure 4 Defect-band emission PL images on wafers 

from different height of a brick: (a) near bottom, (b) 

middle, and (c) near top.

(a) (b)

(c) (d)

(e) (f)

(a) (b)

(a) (b)

(c)
3 

Figure 5 Defect area percentage from InGaAs PL 

images of the finished cell vs. solar cell (a) efficiency 

(b) VOC and (c) JSC.

On all the 30 wafers from each step, we used the same 

threshold to highlight the bright defect regions. The count 

of image pixels above this threshold compared to the total 

number of pixels in an image gives a defect area fraction 

for each wafer. We correlated the defect percentage from 

each step with the finished cell’s parameters, including 

efficiency, open-circuit voltage (VOC) and short-circuit 

current (JSC). An example of the correlation using the 

InGaAs PL images from the finished cells is shown in Fig. 

5. A linear correlation is clearly visible on all three plots. 

The variations of these parameters are about 1% absolute 

in final cell efficiency, 15 mV absolute in VOC, and 1

mA/cm2 absolutely in JSC. 

Similar correlation was seen using defect-band PL images 

from wafers after other processing steps. Fig. 6 shows the

efficiency correlation from as-cut, post-PSG removal and 

post-ARC wafers. The correlations with VOC and JSC still 

hold but are not shown here. It is especially interesting to 

see the correlation on the as-cut wafers, because it 

demonstrates that this defect PL imaging technique can 

be applied on the starting as-cut wafers. Final cell 

efficiencies can be predicted based on defect PL images 

on the as-cut wafers. Incoming wafers can also be sorted 

and sent to different specially designed processing lines to 

optimize the final cells performace. 

 

Figure 6 Defect area percentage from (a) as-cut, (b) 

post-PSG removal, and (c) post-ARC vs. finished cell

efficiency.

(a)

(b)

(c)

(a)

(b)

(c)
4 

SUMMARY

PL imaging with an InGaAs camera was used to study mcSi wafers from partially processed to finished solar cells. 

By mounting different short-pass and long-pass cut-off 

filters, we were able to separate the band-to-band 

emission PL signal from the defect-band emission PL 

signal. The dark areas on the band-to-band images are 

correspondingly bright on the defect-band PL images due 

to the defect-related emission at ~1550 nm from silicon. 

By setting a threshold on these images, we can get an 

area fraction of the bright areas on each defect-band 

emission image. These area percentages were plotted 

against efficiency, VOC and JSC. Good correlations on 

these plots after different processing steps suggest that

defect-band emission PL imaging could be applied as a 

promising technique for inline material monitoring and 

process control.

ACKNOWLEDGEMENT

This work was supported by the U.S. Department of 

Energy under Contract No. DE-AC36-08GO28308 with the 

National Renewable Energy Laboratory. and with support 

from the American Recovery and Reinvestment Act of 

2009. 

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