Focal Auxiliary Classifier Generative Adversarial Network for Defective Wafer Pattern Recognition with Imbalanced Data

Defective wafer pattern recognition is important for quality control and yield enhancement in semiconductor fabrication systems. The collected wafer maps are usually imbalanced, which may degrade the performance of classifier. In this paper, a focal auxiliary classifier generative adversarial network (FAC-GAN) for defective wafer pattern recognition with imbalanced data is proposed. FAC-GAN is composed of AC-GAN modified with focal loss for imbalanced data generation and deep neural network. The performance of the proposed FAC-GAN is measured on real-world wafer map dataset “WM-811k” and it outperforms SVM and CNN.