An oversampling method for wafer map defect pattern classification considering small and imbalanced data

A wafer consists of several chips, and a wafer map shows the locations of defective chips on the wafer. The locational pattern of defective chips on the wafer map provides crucial information for improving the semiconductor wafer fabrication process. Recently, automatic defect pattern classification using convolutional neural networks (CNN) has become popular because of its good classification performance. The good performance is guaranteed only when a large amount of well-balanced training and test data is available. However, such data are difficult to obtain in real practice because the training and test data are obtained by manual inspection. In this paper, we propose a systematic method to resolve the small and imbalanced wafer map data issues. Specifically, we first selected wafers showing clear defect patterns and then replicated them by randomly applying horizontal flip, vertical flip, and rotation. In the case study, we obtained real wafer map data from a semiconductor wafer company. By applying the proposed method, a large amount of well-balanced data was obtained. The CNN model for defect classification was fitted to the obtained data, and it showed good classification performance.

Automated summary

The paper introduces a systematic method to address small and imbalanced wafer map data issues, obtaining a large amount of well-balanced data by selecting appropriate wafers and replicating them multiple times. The CNN model for defect classification shows good performance when applied to the obtained data.