Statistical methods for visual defect metrology

Automated systems are used to inspect unpatterned and product wafers for particulates and other defects. Wafer defect count and defect density statistics are used as process control parameters, but are known to be deceptive in the presence of defect clustering. An improvement path using novel visual defect metrology statistical analyses is proposed. Quadrat analysis, nested analysis of variance, and principal component analysis use data available currently. Spatial point pattern statistics and spatial pattern recognition require special algorithms. Future process control systems made possible by these statistical analyses are discussed.