Detection of semiconductor defects using a novel fractal encoding algorithm

This paper introduces a new non-referential defect detection (NRDD) algorithm for application to digital images of semiconductor wafers acquired during the manufacturing process. This new algorithm is composed of two major steps: (1) defect detection via the use of fractal image encoding and (2) enhanced defect boundary delineation using active contours. One primary application for this technology is the redetection of defects within archived databases of historical defect imagery. Defect images are commonly stored by semiconductor manufacturers for future diagnostic purposes, but reference (golden) images are usually unavailable. The ability to automatically redetect a defect is crucial in an automated diagnostic system that uses the historical defect images for defect sourcing. Results are presented for four large data sets of semiconductor images. Three of these data sets are composed of scanning-electron microscope (SEM) images and the fourth contains optical microscope images. Performance criteria were created that score the NRDD segmentation result as a percentage based on a comparison to a manually outlined version of the defect. The overall NRDD score across all four databases ranged from 50% to 84% using the same set of manually-determined parameters on all images within each database. By using an automated parameter setting algorithm these performance values improved to 57% to 92%. The NRDD algorithm performance depends, in part, on the size of the defect and the level of complexity of the background of the semiconductor image.