Contrastive deep clustering for detecting new defect patterns in wafer bin maps

Wafer bin maps (WBMs) data, presented as images, play a critical role in identifying defects in the semiconductor industry. Thus, accurately classifying WBM defect patterns is essential to maintain high quality and enhance the overall yield. However, the task of labeling and classifying WBM data, which are generated daily in the tens of thousands or more, presents a challenge for experts. Recently, with advancements in artificial intelligence research, there has been a surge in efforts to automatically classify WBM defect patterns. Nevertheless, existing studies have primarily focus on classifying known defect patterns using labels. However, in the real-world semiconductor industry, new defect patterns are constantly emerging in addition to the known patterns. In this study, we propose the contrastive deep clustering (CODEC) for wafer bin maps that identifies new defective patterns in WBMs while simultaneously clustering these patterns into multiple defects without using labels. We use a contrastive loss function to address the challenges associated with a limited number of novel defect patterns. We demonstrate the effectiveness of our proposed methodology in accurately classifying new defect patterns using open data WM-811 k.