KLA Corporation
Unsupervised Learning for Repeater-Defect Detection

Abstract:

To find repeater defects, optical-inspection results for one or more semiconductor wafers are obtained. Based on the optical-inspection results, a plurality of defects on the one or more semiconductor wafers is identified. Defects, of the plurality of defects, that have identical die locations on multiple die of the one or more semiconductor wafers are classified as repeater defects. Based on the optical-inspection results, unsupervised machine learning is used to cluster the repeater defects into a plurality of clusters. The repeater defects are scored. Scoring the repeater defects includes assigning respective scores to respective repeater defects based on degrees to which clusters in the plurality of clusters include multiple instances of the respective repeater defects. The repeater defects are ranked based on the respective scores.