PWS: Potential Wafermap Scratch Defect Pattern Recognition with Machine Learning Techniques

Abstract

Wafermap defect pattern detection and diagnosis provide useful clue to yield learning. However, most wafermaps have no special spatial patterns and are full of noises, which make pattern recognition difficult. Specially, recognizing scratch and line types of defect patterns is a challenging problem for process and test engineers and it takes a lot of manpower to identify such patterns, as potential defective dies may exist on the scratch contour and become discontinuity points. However, such potential defective dies may suffer from latent and leakage faults, which usually deteriorate quickly and need to be screened by burn-in test to improve quality. A possible solution is to locate the obscure defective dies in potential scratch patterns and mark them as faulty. As a result, the quality and reliability of products can be significantly improved and cost of final test can be reduced. In this paper, we propose a systematic methodology to search for potential scratch/line defect types in wafers. A five-phase method is developed to enhance wafermaps such that automatic defect pattern recognition can be carried with high accuracy. Experimental results show the proposed method can achieve more than 89% prediction accuracy for scratch/line types, and higher than 94% for all common wafer defect types.