Influence of wafer quality on cell performance

Carnel, L.; Hjemas, P.C.; Lu, Tse-Hua; Nyhus, J.; Helland, Katherine R.; Gjerstad, Øyvind

doi:10.1109/pvsc.2009.5411761

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Both monocrystalline and multicrystalline silicon (mc-Si) wafers are used extensively for silicon solar cells. While both materials are sensitive to structural defects and impurities mc-Si wafers suffer primarily by the grown-in dislocations in the crystal structure [1]. Hence, it is of great interest to reduce the amount of dislocations formed during crystal-growth as this will directly translate to better cell efficiency, ergo cheaper solar electricity.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of Dislocation-Content in Multicrystalline-Silicon Wafers

Ghaderi

Senkader

2013

SSP

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A major performance limiting factor of multicrystalline silicon wafers is structural defects, mainly dislocations, reducing solar cell efficiency. Dislocations are formed during crystallisation process. Characterization of dislocation-content is necessary both to optimise the crystallisation and to eliminate bad wafers before cell processing. We developed two techniques to characterise dislocations: conventional etch-pit counting modified for full size wafers using a new etch-recipe and a novel etch-pit counting algorithm. Secondly we developed a technique to estimate the dislocation content directly from photoluminescence images of as-cut wafers.

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Section: Introductionmentioning

confidence: 99%

Characterisation of Dislocation-Content in Multicrystalline-Silicon Wafers

Ghaderi

Senkader

2013

SSP

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“…Silicon solar cell performance depends strongly on how well the cell fabrication process is matched to the quality of the wafer source material. Because of the non‐linear effects of cell processing on the electronic properties of the material, the final charge carrier lifetime measured on a solar cell cannot be easily deduced from the initial lifetime measured on the as‐grown wafer 1. Therefore, given the breadth of material quality in the crystalline silicon market–‐from high purity, single crystal (sc‐Si) grown by the Czochralski technique to directionally‐solidified upgraded metallurgical grade multi‐crystalline (mc‐Si) wafers–‐one might expect an equally varied set of manufacturing conditions.…”

Section: Introductionmentioning

confidence: 99%

Impurity‐to‐efficiency simulator: predictive simulation of silicon solar cell performance based on iron content and distribution

Hofstetter

Fenning

Bertoni

et al. 2010

Progress in Photovoltaics

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We present a simulation tool that predicts solar cell efficiency based on iron content in as‐grown wafer and solar cell processing conditions. This “impurity‐to‐efficiency” (I2E) simulation tool consists of three serial components, which are independently and jointly validated using published experimental results: (1) a kinetic model that calculates changes in the distribution of iron and phosphorus atoms during annealing; (2) an electronic model that predicts depth‐dependent minority carrier lifetime based on iron distribution; and (3) a device simulator that predicts solar cell performance based on the minority carrier lifetime distribution throughout the wafer and the device architecture. The I2E model is demonstrated to be an effective predictor of cell performance for both single‐crystalline and multi‐crystalline silicon solar cells. We demonstrate the process optimization potential for the I2E simulator by analyzing efficiency improvements obtained using low‐temperature annealing, a processing concept that has been successfully applied to achieve higher solar cell efficiencies on Fe‐contaminated materials. Copyright © 2010 John Wiley & Sons, Ltd.

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“…Silicon solar cell performance depends strongly on how well the cell fabrication process is matched to the quality of the wafer source material. Because of the non-linear effects of cell processing on the electronic properties of the material, the final charge carrier lifetime measured on a solar cell cannot be easily deduced from the initial lifetime measured on the as-grown wafer [Carnel et al, 2010]. Therefore, given the breadth of material quality in the crystalline silicon market -from high purity, mono-Si grown by the CZ technique to directionally solidified UMG mc-Si -one might expect an equally varied set of manufacturing conditions.…”

Section: Impurity-to-efficiency Simulatormentioning

confidence: 99%

Defect engineering strategies for solar grade silicon and their optimization by predictive simulation

Hofstetter¹

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Facing the shortage of hyper-pure Si due to the exponential growth of the photovoltaic (PV) market at the beginning of the last decade, R&D institutions and Si producers started to develop and apply alternative ways of Si purification for PV applications. The so-called solar grade silicon (SoG-Si) requires lower purity levels than electronic grade Si and therefore, gives space for cost reduction of PV solar energy. However, no standardized specifications of SoG-Si have been established so far and the adaptation of the solar cell fabrication process to lower-quality Si materials remains a challenge for solar cell producers.In this thesis, acceptable concentrations of different representative transition-metal impurities are calculated at different stages of the solar cell production chain: in the final device, in the multi-crystalline (mc) Si wafer, and in the Si feedstock material. Along the production chain, impurity concentrations are assumed to be altered by a standard industrial P diffusion gettering (PDG) during solar cell processing and by solid-liquid segregation during crystal growth. It is shown that the main reduction of total impurity concentrations takes place during crystallization whereas PDG during solar cell fabrication mainly allows the reduction of highly recombination active metal interstitials. Furthermore, it results that much higher concentrations of fast diffusing impurities like Fe and Cu may be present in the Si feedstock than slow diffusing ones like Cr and Ti.Fe is one of the most abundant and detrimental metal impurities in Si. Besides its presence in lower-quality feedstock materials, considerable Fe contamination takes place during mc-Si ingot growth via in-diffusion from the crucible walls. More than 50 % of mc-Si wafers originate from border and edge regions of the ingot so that an effective reduction of Fe during solar cell processing is essential to achieve high conversion efficiencies on mc-Si wafers. Therefore, a model is developed to simulate the kinetics of Fe-related defects during solar cell processing and predict solar cell efficiencies from the as-grown Fe concentration and distribution in mc-Si wafers. The Impurity-to-Efficiency (I2E) model is validated by simulating experiments carried out on wafers from different heights of an intentionally Fe-contaminated mc-Si ingot. The distribution of precipitated Fe in these wafers is measured by means of X-ray fluorescence microscopy and is used as input parameter for the simulations together with total and interstitial Fe concentrations pub-En un segundo experimento, diferentes conjuntos de obleas vecinas mc-Si se someten a diferentes pasos cortos de LTA después del PDG. En estas obleas, el aumento de tiempo de vida y la reducción de Fe i predichos por las simulaciones no se pueden reproducir con los experimentos. Cartografías de tiempo de vida revelan grandes áreas de alta densidad de dislocaciones (DL) que inhiben la extracción eficaz de Fe i y que limitan los tiempos de vida de los electrones. Sin embargo, en algunas de las...

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Influence of wafer quality on cell performance

Cited by 4 publications

References 3 publications

Characterisation of Dislocation-Content in Multicrystalline-Silicon Wafers

Characterisation of Dislocation-Content in Multicrystalline-Silicon Wafers

Impurity‐to‐efficiency simulator: predictive simulation of silicon solar cell performance based on iron content and distribution

Defect engineering strategies for solar grade silicon and their optimization by predictive simulation

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