Recognition of semiconductor defect patterns using spectral clustering

Diverse defect patterns shown on the wafer map usually contain important information for quality engineers to find their root causes of abnormalities. 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 defects still cannot be avoided. This research presents a spatial defect diagnosis system and attempts to solve two challenging problems for semiconductor manufacturing: (1) to estimate the number of defect clusters in advance, and (2) to separate both convex and non-convex defect clusters at the same time. In this paper, a spatial filter is used to denoise the noisy wafer bin map (WBM) and to extract meaningful defect clusters. To isolate various types of defect patterns, a hybrid scheme combining entropy fuzzy c means (EFCM) with spectral clustering is applied to the denoised output. The proposed approach is validated with an empirical wafer bin maps obtained in a DRAM company in Taiwan. Experimental results show that four kinds of mixed- type defect patterns are successfully extracted and classified.