Automatic classification of microlithography macrodefects using a knowledge-based system

Abstract

The benefi ts of automatic classifi cation of microlithography defects include fast and reliable rework decisions, improved root-cause analysis, and more consistent SPC data that signifi cantly enhances yield in the lithography cell. An adaptive knowledge-based system has demonstrated the ability to accurately classify defects more than 85% of the time and is suffi ciently versatile to classify new defect modes that will accompany advanced lithography processes. The knowledgebased system defi nes each class of defects with mathematical descriptors that include categories such as size, intensity, edge sharpness, color, etc. New defect classes can be defi ned with as few as three to fi ve images of the specifi c defect. All defect classes are stored in the knowledge-base as rule vectors consisting of values for each descriptor. Different defect classes can share many common descriptors. However, as long as there is at least one descriptor that differentiates them, the defect class can be deemed unique. This method provides manufacturers the ability to defi ne defects according to their existing rules and to defi ne new defect types as they occur.