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Quality rating of silicon wafers - a pattern recognition approach

: Demant, Matthias
: Weber, E.; Brox, T.

Volltext urn:nbn:de:0011-n-4266085 (8.9 MByte PDF)
MD5 Fingerprint: b01fa5ac27b3411bf854319b6ebb352e
Erstellt am: 6.1.2017


Stuttgart: Fraunhofer Verlag, 2016, IV, 173 S.
Zugl.: Freiburg/Brsg., Univ., Diss., 2016
Solar Energy and Systems Research
ISBN: 978-3-8396-1124-1
Dissertation, Elektronische Publikation
Fraunhofer ISE ()
computer vision; machine learning; testing of materials; Technologie zu erneuerbaren Energiequellen; Rechnersehen; Mustererkennung; Materialbewertung; Solarzellen; Qualitätssicherung; Informatiker; Solarzellenphysiker; Ingenieure im Bereich erneuerbarer Energien

(Topic I) Micro-cracks in silicon wafers reduce the strength of the wafers and can lead to critical failure within the solar-cell production. Especially micro-cracks which are induced before emitter diffusion strongly influence the current-voltage characteristics of the solar cell. To improve accuracy of crack detection in photoluminescence and infrared transmission images of as-cut wafers machine learning techniques are applied. Moreover, the comprehensive set of wafers allows the impact of crack morphology on wafer strength and electrical quality to be investigated and to derive sorting criteria. (Topic II) The efficiency of mc-Si silicon solar cells is sensitive to variations in electrical material quality. For these reasons, a rating procedure based on photoluminescence imaging has been developed within this work. The material quality is characterized by the distribution of crystallization-related defects, which are successfully correlated with the solar cell quality. This is demonstrated by an evaluation of a broad spectrum of currently available materials in a true blind test.