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Visualizing Material Quality and Similarity of mc-Si Wafers Learned by Convolutional Regression Networks
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Matthias Demant and Patrick Virtue and Aditya Kovvali and Stella X. Yu and Stefan Rein
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IEEE Journal of Photovoltaics, 9(4):1073-1080, 2019
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Paper
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Abstract
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Convolutional neural networks can be trained to assess the material quality of multicrystalline silicon wafers. A successful rating model has been presented in a related work, which directly evaluates the photoluminescence (PL) image of the wafer to predict the current–voltage parameters after solar cell production. This paper presents the results of two visualization techniques to understand what has been learned in the network. First, we reveal what has been learned in the PL image by visualizing the spatial quality distribution of the wafers based on the activation maps of the network. The method is denoted as regression activation mapping. We compare regression activation maps with j0 images of solar cells to show the semantically meaningful representation of the trained features. Second, we show what has been learned in the data by mapping the learned network representation of all wafers into a low-dimensional subspace. Visualizations reveal the smoothness of our representation with respect to the PL input and measured quality. This technique can be used to detect material anomalies or process faults for samples with high prediction errors.
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Keywords
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convolutional neural network (CNN), densely connected convolutional neural network (denseNet), fault detection, machine learning, material, multicrystalline silicon (mc-Si), passivated emitter and rear cell, photoluminescence (PL), rating, regression, regression activation mapping, solar cell, t-distributed stochastic neighborhood embedding (t-SNE)
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