The quality assessment of multi-crystalline and high-performance multi-crystalline silicon wafers during incoming inspection of solar cell production requires a reproducible description of the relevant material defects and a classification scheme that is capable to rate as-cut wafers from unknown manufacturers. Both needs are addressed in this work. We introduce an image processing framework that allows the various types of crystallization-related defects visible in photoluminescence images to be detected quantitatively and thus enables a complete wafer description in terms of defects. The importance of different features within this defect characteristic is weighted by predicting the open-circuit voltage of solar cells with aluminum back-surface field as well as passivated emitter and rear cells with a stepwise extension of the model. The resulting robust classification scheme is successfully evaluated on a set of 7500 wafers, which represents the whole spectrum of material types and qualities that are currently available at the market. A comparison of defect signatures in high-performance multi-crystalline and standard multi-crystalline silicon materials underlines the relevance of additional features. As a result of this paper, we show that a regularized version of multi-linear regression models for quality prediction can optimize simpler linear models by adding selected features to the defect characteristic leading to mean absolute prediction errors of 2.2 mV for solar cells with aluminum back-surface field and 2.9 mV for passivated emitter and rear cells solar cells in a true blind test
Realizing narrow contact fingers with low lateral resistance is a major goal for the front-side metallization of silicon solar cells. The formation of screen-or stencil-printed contact fingers is governed by a variety of influencing factors. One of these factors is the surface roughness of the textured silicon wafer. However, only a few investigations have been carried out to investigate this impact in detail. In this study, the influence of arithmetical mean roughness R a of four differently textured wafer surfaces on contact finger geometry and lateral finger resistance, as well as optical and electrical losses, has been investigated. It will be shown that texture roughness has a considerable impact on the properties of the front-side grid. Narrower contact fingers could be realized on the smoothest texture, leading to a current density gain of Δj sc = +0.27 mA/cm 2 . On the other hand, increasing texture roughness has affected the amount of transferred paste and, thus, has led to a lower lateral finger resistance R L . Thus, contact fingers on the roughest texture have benefited from a fill factor gain of ΔFF = +0.24 % ab s . A sensitivity analysis of both impacts has shown that the current density gain has overcompensated the fill factor loss. Thus, textures with a small roughness are beneficial with respect to the formation and electrical properties of stencil-printed front-side grids.
A fast and thorough characterization of grain structure in multicrystalline silicon (mc-Si) is crucial to improve crystal growth and thus bulk lifetime in solar cells. The presented characterization techniques are based on simple optical measurements on as-cut mc-Si wafers. An insight into the entire brick is gained by connecting 2D-information, computed via advanced pattern recognition techniques, over brick height. We identify robust statistical key parameters. Their development within typical bricks of different cast-Si techniques is compared and it is found that the distinct behavior of different materials in the lower part of the brick subsides towards the brick top where grain size distribution is similar
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