Automatic Defect Cluster Extraction for Semiconductor Wafers

Defects on fabricated semiconductor wafers tend to cluster in distinguishable patterns. The ability to accurately identify these patterns allows manufacturers to trace their root causes to a specific process step or equipment. This paper deals with an algorithm that automatically extracts defect clusters. The algorithm performs cluster segmentation and detection by employing two separate and parallel processes. This increases robustness while maintaining high accuracy and speed of data processing. In this paper a new method that allows users to select a tradeoff threshold point between the acceptable false alarm and false rejection rates to suit their applications is introduced.