Automatic identification of spatial defect patterns for semiconductor manufacturing

This research proposes an on-line diagnosis system based on denoising and clustering techniques to identify spatial defect patterns for semiconductor manufacturing. Today, even with highly automated and precisely monitored facilities used in a near dust-free clean room and operated with well-trained process engineers, the occurrence of spatial signatures on the wafer still cannot be avoided. Typical defect patterns shown on the wafer, including edge ring, linear scratch, zone type and mixed type, usually contain important information for quality engineers to remove their root causes of failures. In this paper, a spatial filter is simultaneously used to judge whether the input data contains any systematic cluster and to extract it from the noisy input. Then, an integrated clustering scheme combining fuzzy C means (FCM) with hierarchical linkage is adopted to separate various types of defect patterns. Furthermore, a decision tree based on two cluster features (convexity and eigenvalue ratio) is applied to a separated pattern to provide decision support for quality engineers. Experimental results show that both real dataset and synthetic dataset have been successfully extracted and classified. More importantly, the proposed method has potential to be further applied to other industries, such as liquid crystal display (LCD) and plasma display panel (PDP).