Deep Convolutional Generative Adversarial Networks-Based Data Augmentation Method for Classifying Class-Imbalanced Defect Patterns in Wafer Bin Map

Abstract

In the semiconductor industry, achieving a high production yield is a very important issue. Wafer bin maps (WBMs) provide critical information for identifying anomalies in the manufacturing process. A WBM forms a certain defect pattern according to the error occurring during the process, and by accurately classifying the defect pattern existing in the WBM, the root causes of the anomalies that have occurred during the process can be inferred. Therefore, WBM defect pattern recognition and classification tasks are important for improving yield. In this paper, we propose a deep convolutional generative adversarial network (DCGAN)-based data augmentation method to improve the accuracy of a convolutional neural network (CNN)-based defect pattern classifier in the presence of extremely imbalanced data. The proposed method forms various defect patterns compared to the data augmentation method by using a convolutional autoencoder (CAE), and the formed defect patterns are classified into the same pattern as the original pattern through a CNN-based defect pattern classifier. Here, we introduce a new quantitative index called PGI to compare the effectiveness of the augmented models, and propose a masking process to refine the augmented images. The proposed method was tested using the WM-811k dataset. The proposed method helps to improve the classification performance of the pattern classifier by effectively solving the data imbalance issue compared to the CAE-based augmentation method. The experimental results showed that the proposed method improved the accuracy of each defect pattern by about 5.31% on average compared to the CAE-based augmentation method.