An autoencoder with Gaussian noise injection in its latent space generates synthetic wafer maps to balance classes, and a CNN trained on the augmented data reports 98.56% accuracy on WM-811K with AUC and AP of 1.0000.
Physical Review B 99(4), 041405 (2019)
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Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
An autoencoder with Gaussian noise injection in its latent space generates synthetic wafer maps to balance classes, and a CNN trained on the augmented data reports 98.56% accuracy on WM-811K with AUC and AP of 1.0000.