AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition

Abstract

Identification of the defective patterns of the wafer maps can provide insights for the quality control in the semiconductor wafer fabrication systems (SWFSs). In real SWFSs, the collected wafer maps are usually imbalanced from the defective types, which will result in misidentification. In this paper, a novel deep learning model called adaptive balancing generative adversarial network (AdaBalGAN) is proposed for the defective pattern recognition (DPR) of wafer maps with imbalanced data. In addition, a categorical generative adversarial network is improved to generate simulated wafer maps in high fidelity and classify the patterns with high accuracy for all defective categories. Taking consideration of the various learning abilities of the DPR model for different patterns into account, an adaptive generative controller is designed to balance the number of samples of each defective type according to the classification accuracy. The experiment results indicated that the proposed AdaBalGAN model outperforms conventional models with higher accuracy and stability for the DPR of wafer maps. Further results of comparative experiments revealed that the proposed adaptive generative mechanism can enhance and balance the recognition accuracy for all categories in the DPR of wafer maps.