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

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 presente...