Semi-GAN: An Improved GAN-Based Missing Data Imputation Method for the Semiconductor Industry

Abstract

Complete data are required for the operation, maintenance, and detection of faults in semiconductor equipment. Missing data occur frequently because of defects such as sensor, data storage, and communication faults, leading to reductions in yield, quality, and productivity. Although many attempts have been made to solve this problem in other fields, few studies have specifically addressed data imputation in the semiconductor industry. In this study, an improved generative adversarial network (GAN)-based missing data imputation for the semiconductor industry called Semi-GAN is proposed. This study introduces a machine learning approach for dealing with data imputation in the semiconductor industry. The proposed method was applied to real data and evaluated using traditional techniques. In particular, the proposed method showed excellent results compared to traditional attribution methods when all missing data ratios in the experiments were less than 20%. It was also observed to be superior when simple and repetitive patterns were omitted rather than repetitive but not simple patterns.

Keywords

• Data imputation
• deep learning
• fault classification and detection
• generative adversarial networks
• machine learning
• missing data