Enhanced detection of unknown defect patterns on wafer bin maps based on an open-set recognition approach

Abstract

It is crucial to detect and classify defect patterns on wafers in semiconductor-manufacturing processes for wafer-quality management and prompt analysis of defect causes. In recent years, continuous technological innovation and advancements in semiconductor-industry processes have led to an increase in unknown defect patterns, which must be detected and classified. However, detection of unknown defect patterns is difficult due to complex reasons, such as training on non-existent defect classes, closed datasets owing to industrial security, and labeling large volumes of manufacturing data. Owing to these challenges, methods for detecting unknown defect patterns in an actual semiconductor-manufacturing environment primarily rely on qualitative indicators, such as intuition and experience of engineers. To overcome these problems, this study proposes a methodology based on open-set recognition to accurately detect unknown defect patterns. This methodology begins with two preprocessing steps: constrained mean filtering (C-mean filtering); and Radon transform to diminish noise and efficiently extract features from wafer-bin maps. This study then develops an entropy-estimation one-class support vector machine (EEOC-SVM), which accounts for the uncertainty in the one-class SVM classification results. EEOC-SVM computes entropy-uncertainty scores based on the distance between decision boundaries and samples and then reclassifies uncertain samples using a weighted sum of uncertainties for each class. This method can effectively detect unknown defect patterns. The proposed method achieves a detection performance of over 98 % for various defect classes based on experiments conducted with new defect patterns occurring in actual semiconductor-manufacturing environments. These results confirm that the proposed method is an effective tool for detecting and addressing unknown defect patterns.