Automatic Defect and Signature Classification ADC was initially developed in the early '90s to automate the manual classification of defects during off-line optical microscopy review. Standard practice has been to apply proximity clustering that result in a single event being represented as many unrelated clusters. SSA performs data reduction by clustering defects together into extended spatial groups and assigning a classification label to the group that reflects a possible manufacturing source. SSA technology has also been extended to analyze electrical test binmap data to recognize process-dependent patterns. The basic input to all of the classifier approaches are descriptive features.
Features are numerical or syntactic descriptions of the defect object within an image.
Numerical features include measurements of the defect image intensity, texture, morphology, color, etc.