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Learning Quality Rating of As-Cut mc-Si Wafers via Convolutional Regression Networks

: Demant, M.; Virtue, P.; Kovvali, A.; Yu, S.X.; Rein, S.

Postprint urn:nbn:de:0011-n-5584763 (2.6 MByte PDF)
MD5 Fingerprint: a0b6d2315d92749cd5433740a27b8bd7
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Erstellt am: 21.11.2019

IEEE Journal of Photovoltaics 9 (2019), Nr.4, S.1064-1072
ISSN: 2156-3381
ISSN: 2156-3403
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer ISE ()
Photovoltaik; Silicium-Photovoltaik; Charakterisierung von Prozess- und Silicium-Materialien; Messtechnik und Produktionskontrolle; CNN; regression; DenseNet; imaging; detection; Inline-Wafer-/Prozessanalytik und Produktionskontrolle

This paper investigates deep convolutional neural networks (CNNs) for the assessment of defects in multicrystalline silicon (mc-Si) and high-performance mc-Si wafers for solar cell production based on photoluminescence (PL) images. We identify and train a CNN regression model to forecast the I - V parameters of passivated emitter and rear cells from given PL images of the as-cut wafers. The presented end-to-end model directly processes the PL image and does not rely on the human-designed image feature. Domain knowledge is replaced by a model based on a huge variety of empirical data. The comprehensive dataset allows for the evaluation of the generalizability of the model with test wafers from bricks and manufacturers not presented in the training set. We achieve mean absolute prediction errors as low as 0.11 %abs in efficiency for test wafers from 'unknown' bricks, which improves handcrafted feature-based methods by 35%rel at simultaneously lower computational costs for prediction. Samples with high prediction errors are investigated in detail showing an increased iron point defect concentration.