Automatic Inspection for Wafer Defect Pattern Recognition with Unsupervised Clustering

We propose an automatic wafer defect maps detection method based on unsupervised learning. There is no need for human labeling, and similar defect clusters are identified automatically without human intervention. As a result, the process is less error-prone. Whenever the wafer test result of a WUT is available, it can be compared immediately with existing clusters. If the wafer map matches one of the known defect patterns, then RCA can be done efficiently.