Semiconductor yield improvements through automatic defect classification - Final Report

Abstract

Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. Projections by semiconductor manufacturers predict that with larger wafer sizes and smaller line width technology the number of defects to be manually classified will increase exponentially. This cooperative research and development agreement (CRADA) between Martin Marietta Energy Systems (MMES) and KLA Instruments developed concepts, algorithms and systems to automate the classification of wafer defects to decrease inspection time, improve the reliability of defect classification, and hence increase process throughput and yield. Image analysis, feature extraction, pattern recognition and classification schemes were developed that are now being used as research tools for future products and are being integrated into the KLA line of wafer inspection hardware. An automatic defect classification software research tool was developed and delivered to the CRADA partner to facilitate continuation of this research beyond the end of the partnership.