Image processing techniques for wafer defect cluster identification

Abstract

Electrical testing determines whether each die on a wafer functions as originally designed. But these tests don't detect all the defective dies in clustered defects on the wafer, such as scratches, stains, or localized failed patterns. Although manual checking prevents many defective dies from continuing on to assembly, it does not detect localized failure patterns-caused by the fabrication process-because they are invisible to the naked eye. To solve these problems, we propose an automatic, wafer-scale, defect cluster identifier. This software tool uses a median filter and a clustering approach to detect the defect clusters and to mark all defective dies. Our experimental results verify that the proposed algorithm effectively detects defect clusters, although it introduces an additional 1% yield loss of electrically good dies. More importantly, it makes automated wafer testing feasible for application in the wafer-probing stage.