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However, in most wafers, the proportion of defective dies is much lower than that of normal dies, and the proportion of defects that occur according to the defect pattern is also not constant, so a data imbalance problem occurs.
In this paper, we propose a Convolutional Autoencoder (CAE) using skip connection to solve the above-mentioned problem.
To improve the CAE performance, the architecture is deeply designed with skip connection, and data augmentation is performed by controlling the CAE to learn how to express data more efficiently.
Then, the learned CAE encoder is recycled to encode the training data input to the classifier, and then the classifier is trained.
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IEEE/ICACT20220181 Slide.05
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