Quality control of as-cut multicrystalline silicon wafers using photoluminescence imaging for solar cell production

Solar Energy Materials & Solar Cells ] (]]]]) ]]]–]]]

Contents lists available at ScienceDirect

Solar Energy Materials & Solar Cells

0927-02

doi:10.1

n Corr

E-m

Pleas

journal homepage: www.elsevier.com/locate/solmat

Quality control of as-cut multicrystalline silicon wafers usingphotoluminescence imaging for solar cell production

Jonas Haunschild n, Markus Glatthaar, Matthias Demant, Jan Nievendick,Markus Motzko, Stefan Rein, Eicke R. Weber

Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstr. 2, 79110 Freiburg, Germany

a r t i c l e i n f o

Article history:

Received 7 April 2010

Received in revised form

6 May 2010

Accepted 6 June 2010

Keywords:

Multicrystalline silicon

Silicon solar cells

Lifetime measurement

Photoluminescence imaging

Material quality

48/$ - see front matter & 2010 Elsevier B.V. A

016/j.solmat.2010.06.003

esponding author.

ail address: [email protected]

e cite this article as: J. Haunschild,

a b s t r a c t

Measuring the lifetime of excess charge carriers gives the opportunity to access the electric quality of

the material. However, on as-cut wafers before production this quantity is strongly limited by the

surface of the material. On a batch of solar cells we show that the open circuit voltage of the finished

cells only scales with the lifetime, measured on as-cut wafers, if the material quality is very low. The

difference between moderate and high material quality cannot be resolved. Using photoluminescence

imaging the lifetime can be acquired with high spatial resolution. We show that by analyzing

crystallization-related features in the images, certain defects can be identified: Such features are defects

of crystal growth (e.g. dislocations) and areas of reduced lifetime that form at the edges of the

crystallization crucible or near the top or bottom of a brick. Those features can be detected before

production and we show their influence on cell parameters. By recognizing and rating these features,

a more accurate quality control for wafers can be introduced.

& 2010 Elsevier B.V. All rights reserved.

1. Introduction

Photovoltaic industries are growing rapidly worldwide. Demandson systems for quality control are growing as well, especially forsystems that are already applicable on as-cut wafers before solar cellproduction. A meaningful rating of the material quality is veryimportant for cell manufacturers, because low solar cell efficienciescan be directly attributed to low material quality and process-related problems can be excluded. Some manufacturers of waferinspection systems already integrate different tools for measuringthe effective lifetime of excess charge carriers. However, we willshow that it is of little benefit to use this quantity, as it does notnecessarily scale with later cell performance. Only very low materialquality can be identified.

Photoluminescence (PL) imaging [1] has been introduced as afast tool to image the lifetime of a wafer. In recent years, it hasbeen continuously improved to give access to different properties(e.g. diffusion length [2] or iron contamination [3]) and to beapplicable in different steps of the production line [4,5].

In this publication we show in a first step experimentally andtheoretically that lifetime measurements, yielding a global value,cannot be taken as measure for material quality (except in thecase of very poor material). In a second step, we show thatefficiency limiting defects, which can occur at crystallization, can

ll rights reserved.

e (J. Haunschild).

et al., Sol. Energy Mater. So

be seen in the PL images. Via correlation with the open circuitvoltage of the finished cells, we prove how these features can beused for quality control on as-cut wafers.

2. Setup and experiment

Trupke et al. [1] described the experimental setup needed toperform photoluminescence imaging. In the system developed atFraunhofer ISE, the sample is positioned on a chuck, which istemperature-stabilized to 25 1C. The whole wafer area is homo-genously illuminated by a laser radiating at 790 nm, with anintensity equivalent to a total charge carrier generation of up to twosuns. From luminescence radiation with its peak at 1150 nm, onlythe short-wavelength part is detectable with silicon charge coupleddevice (Si-CCD) cameras and therefore – depending on the camera’squantum efficiency – rather long exposure times are necessary. Toachieve data acquisition times o1 s, we use a cooled Si-CCD cameraand a lens that have been optimized for infrared light beyond1000 nm. Please note that exposure times are strongly dependent onexperimental conditions and used hardware. The camera is mountedabove the sample and can be moved closer to it to image a sectionwith higher resolution. Reflected laser light is suppressed by a stackof long pass filters in front of the camera lens.

QSSPC [6] measurements are performed on a Sinton ConsultingWCT-100 tool and base resistance is measured on a Kitec PV-Rsystem.

l. Cells (2010), doi:10.1016/j.solmat.2010.06.003

www.elsevier.com/locate/solmat

dx.doi.org/10.1016/j.solmat.2010.06.003

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J. Haunschild et al. / Solar Energy Materials & Solar Cells ] (]]]]) ]]]–]]]2

The paper is based on experiments carried out on differentbatches of solar cells that were processed following a standardprocess: acidic texturing, two sided emitter diffusion, phosphorsilicate glass etching, anti-reflection-coating, screen-printing, fastfiring and laser edge isolation.

Multicrystalline silicon (mc-Si) wafers were chosen fromdifferent positions in the brick or the crystallization crucibleand sorted into corresponding groups. For each wafer, theresistivity ranging from 0.9 to 1.35 Ohm was measured via eddycurrent probing from an average of three recorded tracks perwafer.

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Bulk Lifetime

Bulk Lifetime from PL (µs)

Ope

n C

ircui

t Vol

tage

(mV

)

S = 10 cm /sW = 200 mD = 28 cm /s

Effective Lifetime

Effective Lifetime from PL (µs)

Fig. 1. The plot of bulk- and effective lifetime, measured on as-cut wafers and the

VOC-values, measured on the finished cells, shows no correlation except for the

wafers in the lower left corner, which resulted in low VOC.

3. Scaling PL images to lifetime

Photoluminescence of silicon wafers is caused by radiativerecombination of photo-excited electron–hole pairs. The radiativecomponent of the recombination of excess charge carriers Dn canbe detected with a Si-CCD camera. In approximation, themeasured intensity IPL of a pixel depends on Dn, the doping Ndop

of the wafer and a calibration constant c comprising the opticalproperties of the setup and the sample. For low-injectionconditions it follows [7]:

IPL ¼ cDnNdop: ð1Þ

The lifetime t of the minority charge carriers follows witht¼Dn/G, where G is the generation rate of the photo-excitedcharge carriers. For an exact determination of t, the depthdependence of Dn, G and the reduction of IPL through reabsorptionof luminescence radiation in the sample is necessary to be known.In simplified approaches the signal can be calibrated to lifetime byindividually fitting the spatially averaged PL intensity to thelifetime determined by a reference technique [8,9] for each wafer.Quasi-steady state photoconductance (QSSPC) is used for cali-brating the PL intensity in most cases. Recently Giesecke et al. [10]showed how a calibration to bulk lifetime can be done fromsimulations of the excess charge carrier density based on thecontinuity equation and the generalized Planck law of radiation[11], without the need of calibrating to a reference technique—

however still the doping of the sample and its surface propertiesneed to be known.

For inline application, the first approach can be sufficient;however, when testing low lifetime samples (e.g. as-cut material),trapping becomes a problem for photoconductance measure-ments. As PL measurements are not influenced by trapping effects[12], photoconductance-calibrated PL images can again beinfluenced by trapping.

Therefore, we will choose another approach of calibrating thePL images to effective lifetime: For each wafer, the doping NDop ismeasured via resistivity measurement and the optical constant c

is gained from a set of calibration wafers as presented in thefollowing. The base resistance, from which the doping level iscalculated, is not liable to trapping, so we get a fast and reliablemethod of calibration.

We acquire the optical properties of the setup (cameraquantum efficiency, filter transmission, excitation intensity, etc.)through a set of as-cut calibration wafers that have beenmeasured with QSSPC. By correcting the PL intensity with respectto doping, the resulting value can directly be taken as a quantityproportional to the effective lifetime according to Eq. (1). Theresult can be scaled to a reference lifetime technique, simply by ascaling factor [12,13]. This scaling factor can be acquired byplotting the doping corrected PL intensity against the QSSPClifetimes for a set of calibration wafers. A linear fit yields thescaling factor, which is identical to the calibration constant c.

Please cite this article as: J. Haunschild, et al., Sol. Energy Mater. So

In this approach, we treat the surface properties (opticalproperties [14], surface recombination velocity [12], etc.) for allas-cut wafers alike and also include them into the calibrationconstant. The scaling factor, which is gained in this way, is validfor all as-cut wafers with comparable surface properties. Pleasenote that for wafers of other stages in processing, a newcalibration needs to be done.

4. Rating material quality by lifetime

Before processing the wafers to cells, the wafers weremeasured with PL, QSSPC and eddy current probing. The PLmeasurements were calibrated to effective lifetime as shown inthe previous section. The bulk lifetimes were calculated for eachpixel using the analytical approach of Grivikas et al. [15]:

1

tbulk¼

1

teff�

1

tsurf þtdiff, ð2Þ

with tsurf¼W/2S being the surface dependent part and tdiff¼D(w/p)2

the diffusion dependent part. We assume a surface recombinationvelocity (SRV) of S¼106 cm2/s, a thickness of w¼200 mm, a diffusionconstant of D¼28 cm2/s and average the lifetime over the wholewafer area. From I–V curve measurements on the finished cells,which have been processed from these wafers, we gain the electricparameters of the cells. In Fig. 1, we compare effective- and bulklifetimes measured on as-cut material with the open circuit voltage(VOC) of the finished cell.

In comparison to VOC we find no correlation, except for a fewwafers with very low lifetimes which resulted in a VOC of approx.595 mV (indicated by the green circle). A correlation to QSSPClifetimes was already presented in Ref. [13] and also showed nocorrelation. The reason for this behavior is that effective lifetimesmeasured on as-cut material – independent of the measurementtechnique – are strongly surface dominated. In Fig. 2, thecorrelation of effective lifetime to bulk lifetime is plottedfollowing Eq. (2) for different SRVs: In as-cut material theeffective lifetime is saturated very quickly for high SRVs.Evaluating bulk lifetimes in this regime will lead to drasticerrors due to noise of the measurements. Hence, a slightly highereffective lifetime will be misinterpreted as a much higher bulklifetime. Only if the diffusion length is much smaller than the



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e (µ

s)

Bulk Lifetime (µs)

W = 200 µm

Dn = 28 cm2 /s

SRV = 106 cm2/s

10 15 20 25 30 35 40 45 50

Fig. 2. Plot of the effective lifetime against the bulk lifetime for different surface

recombination velocities. For as-cut wafers (highest SRV), the correlation saturates

quickly and no statement can be made on bulk lifetime.

J. Haunschild et al. / Solar Energy Materials & Solar Cells ] (]]]]) ]]]–]]] 3

thickness of the wafer, the influence of the surface is not asdominant and a correlation to bulk lifetime or VOC can be made.Giesecke et al. [16] showed the special relation of bulk lifetimeand PL intensity for different SRVs leading to the same conclusion.

Another source for weak correlation is that bulk lifetime isimproved through the process. Phosphorous gettering at theemitter diffusion and hydrogen gettering at the fast firing canremove some of the impurities and improve the bulk lifetime.

Similar results have already been shown by Sinton et al.[17]and Trupke et al. [7]; therefore in general, the question arisesas to what informative value is presented by lifetimes measuredon as-cut wafers. Bulk defects appear in PL images of as-cutwafers only if they outrun the contribution of surface recombina-tion. Hence, taking the global or local lifetime from as-cut wafersis only a reliable measure for the quality of very poor wafers.

Fig. 3. A line scan is done as illustrated in Fig. 4a on a PL image of an as-cut wafer

(black), and the wafer after emitter diffusion (red). In comparison, we observe that

15 mm of the edge region was gettered. (For interpretation of the references to

colour in this figure legend, the reader is referred to the web version of this

article.)

Fig. 4. PL images of three material groups taken for the edge-feature comparison.

The images show the complete 156�156 mm2 wafer (resolution: 640 mm per

pixel, exposure time: 0.5 s).

5. Rating material quality by features extraction

Now we are going to introduce an alternative procedure,which allows gaining information from PL images of as-cutwafers: A variety of defects in crystal growth is visible in PLimages and can be directly attributed to a loss in VOC. In thefollowing, we will demonstrate this correlation using theexamples of bottom- and edge-regions and dislocations inmc-silicon.

5.1. Edge zones

Block-cast mc-Si wafers exhibit areas of reduced lifetime in thevicinity of the edges of the crystallization crucible due toimpurities diffusing from the crucible wall into the silicon meltduring solidification. Rinio et al. [18] showed that in thisdeteriorated layer the dominating impurity is interstitial iron.Additionally, Naeland et al. [19] showed that for the reducedlifetime neither dislocations, nor inclusions of carbides andnitrides are responsible. In PL images the area of reduced lifetimeis easily visible. In this case with a thickness of approx.15–20 mm, depending on how much was cut away at theseparation of the bricks. In most cases also within a brick thethickness of the deteriorated layer reduces, as impurities havemore time to diffuse into the already solidified lower part of theblock. After identifying iron as a major contaminant, Rinio et al.


[18] showed that through phosphorous gettering at the emitterdiffusion the contaminations can effectively be gettered. We canreport the same results from our experiment: Fig. 3 shows a linescan from the middle of a wafer to the edge. The line scan wasdone on a PL image of the as-cut wafer and on the same waferafter emitter diffusion. Through gettering, the edge zone wasreduced by ca. 15 mm; however, it could not be removedcompletely. In Fig. 4 three wafers are shown, taken from threedifferent bricks that have been crystallized at the same facility.One is taken from a corner (a) of the crucible, one from an edge (b)and one from the center (c). The PL images show the completewafer and were taken in 0.5 s. Although gettering reduced theeffect of these edge zones, we can still report a drop in VOC of thefinished cells. In Fig. 5 the results from I–V curve characterizationare plotted, showing that about 2 mV per edge were lost in thisexperiment. If gettering is not strong enough – as can be the casefor fast inline diffusions – the effect on VOC can be even worse.

5.2. Top and bottom regions

Top and bottom of block-cast silicon bricks show a well-knownreduction of lifetime [20]. The reason for this is, as in the edgezone case, impurities diffusing from the crucible into the melt(bottom) [21], or segregated intrinsic impurities of the feedstock(bottom and top). In the top region the reduced lifetime originatesfrom dissolved impurities, which segregate during solidification



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Edge614 mV16.2 %9

Center616 mV16.4 %8

Position:Mean Voc:Efficiency:Samples:

Corner612 mV16.1 %5

Fig. 5. The I–V characterization shows that the influence of the edge features on

the VOC was approx. 2 mV per edge in this experiment. The diagram shows

smallest-, largest- and mean-value.

Fig. 6. PL images of wafers from different positions in the brick, as marked in

Fig. 7. Displayed is a 30�30 mm2 section of the images (resolution: 160 mm per

pixel, exposure time: 60 s).

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Fig. 6b Open Circuit Voltage Guideline to the eye Contrast Grain / Grain Boundary

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Brick Height (%)

Fig. 6a-15-10-5051015

Con

trast

(%)

Fig. 7. Plotted is the VOC of the finished cells against the effective lifetime (upper

graph) and the contrast of grain to grain boundary (lower graph), evaluated from

the PL images of the as-cut wafers. While both show a good correlation, the

evaluation with respect to contrast is independent of SRV.

Fig. 8. A high-resolution PL image of a wafer from the bottom: the camera was

moved closer to the sample to achieve a higher spatial resolution. Different

gettering activities of grain boundaries and dislocations can be observed

(resolution: 50 mm per pixel, exposure time: 600 s).


to the surface of the melt and after solidifying may diffusebackwards into the silicon. Therefore, top and bottom metalliccontamination need not be the same.

We apply PL imaging on wafers taken from different positionsof the lower part of a brick. In Fig. 6, PL images show a30�30 mm2 section of the wafers. In Fig. 6a, the wafer nearestto the bottom, we find an inverse contrast where the grainboundaries appear brighter than the grains. In Fig. 6b, theimpurity level inside the grain and inside the grain boundaryare about the same, which leads to a very small contrast in the PLimage. In Fig. 6c, a wafer from approx. 1/3 of the brick height, wefind the typical case: bright grains, surrounded by dark grainboundaries. This contrast is not dependent on SRV. To comparethis feature to VOC, we average the PL intensity from the center ofa grain and from a grain boundary. The division yields thecontrast. It is negative for darker grains and positive for brightergrains. In Fig. 7 the grain-contrast, taken from the PL images of theas-cut wafers and the VOC of the finished cells is plotted. Thecontrast follows the voltage and also saturates at approx. 20% ofbrick height. The plot of VOC and lifetime shows similar results.Please note that this graph is equivalent to Fig. 1; however, onlythe datapoints of the bottom-region material are shown. Thelifetime of the as-cut wafers correlates to VOC only for this specialcase where sister wafers from the same brick were taken. Instandard industrial processing environments, the situation of amixture of different material from different positions of bricks, asin Fig. 1, is given. The contrast of grain to grain boundary becomesnegative only for bottom material and thus can be used to identifysuch wafers. Both curves (lifetime or contrast) do not followexactly the VOC. This is a direct result of gettering during theemitter diffusion. The demonstrated results of PL imaging haveonly been obtained for bottom-region material and still need to bechecked for top-region material.

To answer the question of why the contrast is inverting in thebottom region, we move the camera closer to the wafer to image asection with higher spatial resolution. In Fig. 8, a section of awafer from the bottom is enlarged, showing that grain boundariesare surrounded by a region of enhanced lifetime. In this so-calleddenuded zone, impurity atoms are intrinsically gettered into grainboundaries and dislocations [22]. When taking a closer look, it isobservable that not all structures have the same getteringbehavior. In this case the larger structures, which are probablygrain boundaries, do getter, while the smaller structures(probably dislocations) are not gettering. This indicates differentrecombination strengths of these structures and has beeninvestigated in detail by Rinio et al. [23].

Please cite this article as: J. Haunschild, et al., Sol. Energy Mater. Sol. Cells (2010), doi:10.1016/j.solmat.2010.06.003


595 600 605 610 615 62080

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Fraction of Crystal Defets Linear Fit

Frac

tion

of C

ryst

al D

efec

ts (a

.u.)

Open Circuit Voltage (mV)

Fig. 10. Fraction of crystal defects FCD derived from the as-cut wafers plotted

against the VOC of the finished solar cell. A good correlation can be stated.

J. Haunschild et al. / Solar Energy Materials & Solar Cells ] (]]]]) ]]]–]]] 5

5.3. Area of crystal defects

While edge zones and top and bottom regions can be cut off, athird group of defects, which is visible in PL images, is alwayspresent: During crystal growth of mc-Si, grains are formed. Atthe boundaries, where different grains meet, strain fields attractcontaminations, leading to an increased recombination activity[24]. In order to reduce this undesirable effect, grains need to bebig enough in size [25]. Too large grains, however, are more liableto thermal stress, which is a consequence of an inhomogeneoussolidification and temperature gradients disturbing the crystal-lization process [26]. Subgrain boundaries, small sized grains anddislocations are the consequence. As these defects of crystalgrowth cannot be strictly separated, we will treat them alike inthe following discussion.

The effect of dislocations has already been investigated indetail: Through Secco etching [27], dislocations can be madevisible to the naked eye. Rinio et al. [28,29] compared thedislocation density derived from Secco etched wafers with LBICmaps. Other groups used electron microscopy or electron beaminduced current [30–32]. Necessarily the sample sizes have to besmall and an extensive preparation is needed. By using PLimaging, no sample preparation or extensive measurements areneeded on the whole wafer is imaged.

Through image processing, the fraction of crystal defects canbe extracted from PL images. In Fig. 9, we show an example of onewafer that exhibits regions with different quantities of dislocationlines. In this example, we use a short pass filter for spatial imagefeatures to find the edges of the line-like features and to eliminatedisturbing lifetime contrasts. Alternatively, one could usevariance filters, edge-finding algorithms, etc. The fine lines ofcrystal defects remain. We define the pixels covered by crystaldefects divided by the total number of pixels of the wafer as the‘‘fraction of crystal defects’’ (FCD). The algorithm is applied to thewhole wafer and repeated for all wafers used in the experiments.It fails for top/bottom, or edge feature material, because thesefeatures are misinterpreted and skip it in our evaluation. Byusing a more advanced algorithm, these materials could also beanalyzed. A comparison to the results of I–V curve measurementsof the finished cells shows their impact on the voltage. The plot ofFCD against the VOC of the cell yields the graph of Fig. 10. Although

Fig. 9. In this PL image of a 156�156 mm2 as-cut wafer the line-like features

correspond to grain boundaries and dislocations. Through image processing

algorithms these features can be separated from lifetime contrasts. The enlarged

sections show different dislocation densities (resolution: 160 mm per pixel,

exposure time: 60 s).


the approach is kept very simple, the correlation is clearly visible.Note that here a parameter measured before production iscompared to a parameter of the finished cells. The difference ofthe wafers with the lowest FCD and the highest is about 18 mV.This result shows how important crystal defects are and whateffect they have on the cell efficiency.

6. Conclusion

In this paper, different approaches to access the electric qualityof silicon wafers before producing solar cells were discussed. Weshowed experimentally and theoretically that the effective life-time – whether local or global – is too surface- and process-dependent to serve as a reliable measure for material quality. Evenif bulk lifetimes are calculated, they do not correlate to the opencircuit voltage of the finished cells, because even noise inducedvariations are misinterpreted as high differences in the resultingbulk lifetime, due to the high surface recombination velocity

By analyzing not the absolute lifetimes, but the contrasts andfeatures in PL images, we showed that a powerful possibility oftesting the electric quality of the wafers is available. Theprocedure was shown for three different examples: (1) lifetimereductions from the edges of a crucible are clearly visible in theimages. To rate this kind of defect from PL images, one only needsto know the gettering efficiency at their emitter diffusion. If thematerial is comparable, the reduction of VOC can be estimatedsimply by extracting the fraction of the affected area in theimages. (2) Top and bottom regions of bricks lead to a lowintensity in PL images. Additionally, grain boundaries appearbrighter than the grains, due to intrinsic gettering. This contrastchanges depending on the level of impurity and correlates withthe open circuit voltage of the finished cells. (3) Finally,dislocations appear in the images as line-like features and canbe extracted using image-processing algorithms. In our experi-ments the open circuit voltage of the wafer with the mostdislocations was 18 mV smaller, compared to the best wafer.

Wafer manufacturers can use these results to optimize thecrystallization, without the need to wait for cell results. Shorterfeedback loops can be formed and more detailed information onthe crystalline properties of the wafers is provided. Cellmanufacturers can use them to have a quality control beforeproduction.




Acknowledgements

This work has been partially supported by the FraunhoferSociety within the framework of the project SiliconBeacon and theGerman Ministry for the Environment, Nature Conservation andNuclear Safety (BMU) within the framework of the projectQUASSIM.

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