Automated inspection of IC wafer contamination

This paper addresses the task of automating the visual inspection of contam ination on the surface of integrated circuits (IC) wafers arising from the dicing process. Using a set of multi-spectral optical filters and a charged coupled device (CCD) video camera, several images are acquired from each I C wafer under different illumination conditions, from which feature space d ata an then generated. Three conventional classification methods - an artif icial neural network (ANN) using a back-propagation (BP) technique, a minim um distance algorithm, and a maximum likelihood classifier are evaluated, a nd their performances are compared. In addition, important elements of the feature space, i.e., the optimal illumination condition and appropriate opt ical spectrum are investigated. The results show that the image-acquisition technique developed is effective in discriminating feature elements, and t hat the employed ANN-BP classifier can accurately achieve the required bina ry (clean/containinnted IC wafers) decisions. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. AII rights reserved.