An automated defect detection system for silicon carbide wafers

Manual methods of defect detection and classification on silicon carbide (SiC) wafers are tedious, time consuming, and prone to errors. Recently, the SiC Research Group at the University of South Carolina has developed a nondestructive optical stress technique (photoelasticity) to isolate structural defects on silicon carbide wafers. The technique is rapid, non-destructive and inexpensive. In this paper we present an image processing system that exploits the group\´s technique to detect structural defects on SiC wafers. We are specifically interested in detecting defects known as "micropipes". We model the stress birefringence image of the wafer as a 3-D surface, the pixel intensity being the third dimension