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A novel clustering and declustering algorithm for fuzzy classification of wafer defects

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2 Author(s)
T. A. El Doker ; Dept. of Electr. Eng., Northern Arizona Univ., Flagstaff, AZ, USA ; D. R. Scott

A method has been developed for enhancing the efficiency and accuracy of wafer defect analysis for yield improvement. This multi-step fuzzy algorithm has been developed for automatic clustering and classification of wafer defects. The algorithm utilizes a combination of new and existing feature measurements to identify and match defects with those referenced in a defect classes library. The process is more efficient than other approaches like pair-wise K-Nearest Neighbor (K-NN) classifiers and other fuzzy methods, which can be computationally very expensive. The algorithm also offers improved accuracy and the ability to decluster defects in cases where more than one overlap.

Published in:

University/Government/Industry Microelectronics Symposium, 2003. Proceedings of the 15th Biennial

Date of Conference:

30 June-2 July 2003