A Study on Feature Extraction and Ensemble Learning for Wafer Map Classification and Detection of Unknown Defects

Abstract

In semiconductor manufacturing, circuits on the silicon wafers are inspected in various ways, and the distribution of inspection results is obtained as a wafer map. Since the obtained patterns of wafer maps depend on manufacturing error(s), thus the classification of wafer maps and identification of their causes are essential from the viewpoint of production control. There are many studies on wafer map classification, and methods using Convolutional Neural Networks (CNNs) have been proposed. However, these conventional methods assume only defect patterns that have occurred in the past, and these methods cannot detect unknown patterns, which are unexpectedly occurred and obtained in the actual manufacturing sites. Thus, detecting unknown patterns and classifying known patterns with high accuracy is essential to improve the current yield analysis. In this study, the authors proposed a classification system using plural binary classifiers and investigated a feature extraction for the method. In the method, binary classifiers specialized for each known defect pattern were used, and these were concatenated based on each precision value. We conducted evaluation experiments using WM811K dataset. The results showed that the classification accuracy was 77.1% for known defective patterns, and 30.3% of unknown patterns were picked up with the proposed method.