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.
In: 2023 IEEE Inter- national Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp
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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.