P‐58: Novel Defect Data Augmentation in Display Manufacturing Process

Abstract

In this paper, we propose novel defect data augmentation procedure to overcome imbalanced class in development of machine learning models for display manufacturing process. As the amount of defective samples is extremely small compared to that of good samples, it causes severe overfitting and poor performance of models. In addition, numeric or tabular data augmentation is not suitable by GAN or VAE algorithms and its accuracy or similarity is lower than that of image augmentation. To settle this problem, we utilize both over and under sampling, and introduce anomaly score to figure out which process or equipment is in anomaly state. Kernel function or XAI algorithms are also utilized to discover effective features which differentiates defective samples and normal samples. After these process, we use to CT‐GAN to increase the number of defect datasets. In this way, we have demonstrated the enhanced the performance of GAN algorithm in tabular and numeric data.