[1O5-GS-7] Vision, speech media processing: clustering / generation

[1O5-GS-7-01] Imbalanced wafer map data classification by deep learning using master images

〇Takayuki Yamauchi¹, Yuichi Tokuyama¹, Tomoya Kawai¹, Hidetaka Eguchi¹, Gaku Sudo¹ (1. Kioxia Corporation)

Keywords:deep learning, imbalanced data, master image, wafer map

Classification with imbalanced class distributions is a major problem in machine learning. It is well known as the imbalanced data problem. A known effective method to handle this problem is equalizing the number of data for each class by upsampling or downsampling. Another is reflecting the weight according to the number of data in each class in the learning process. However, these methods may cause decreases in the generalization performance of the deep learning models and lack of information during learning, and result in decreases in classification accuracy.
In this study, we are investigating a learning process of the deep learning model using typical images (master images) that capture the characteristics of images for each class. In this paper, the effect of the proposed method on the imbalance data problem is shown by applying it to the unbalanced data of semiconductor wafer defect map.