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Analysis of luminescence images applying pattern recognition techniques

: Demant, M.; Glatthaar, M.; Haunschild, J.; Rein, S.

Fulltext urn:nbn:de:0011-n-1563510 (459 KByte PDF)
MD5 Fingerprint: d69394253b93046a0dc802b25efc606f
Created on: 20.12.2014

European Commission:
25th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC 2010. Proceedings : 5th World Conference on Photovoltaic Energy Conversion, 6-10 , September 2010, Valencia, Spain
München: WIP-Renewable Energies, 2010
ISBN: 3-936338-26-4
European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC) <25, 2010, Valencia>
World Conference on Photovoltaic Energy Conversion <5, 2010, Valencia>
Conference Paper, Electronic Publication
Fraunhofer ISE ()
PV Produktionstechnologie und Qualitätssicherung; Silicium-Photovoltaik; Charakterisierung von Prozess- und Silicium-Materialien; Pilotherstellung von industrienahen Solarzellen; Messtechnik und Produktionskontrolle; Industrielle und neuartige Solarzellenstrukturen; Produktionsanlagen und Prozessentwicklung; Charakterisierung; Qualitätssicherung und Messtechnikentwicklung: Material; Zellen und Module

In this paper we present a novel method to describe the quality of multi-crystalline as-cut wafer based on photoluminescence imaging (PL). PL has a high potential to detect efficiency relevant defects already on as-cut wafers. Defects that can be detected are for instance crystal dislocations and contaminations from iron precipitates or the crystallization crucible. We present reliable image processing algorithms to detect and quantify quality features related to specific defects. For an interpretable presentation the quality features are combined in a histogram. We show that the histogram contains a large fraction of the physically relevant information to predict the open circuit voltage of the finished solar cells by an artificial neural network. This proves that the features can be used to establish a meaningful rating of the wafer quality.