Neural decision directed segmentation of silicon defects

A system is proposed for recognizing four types of defects present in silicon wafer images. After preprocessing, the system applies four segmentation algorithms, one per defect type. Approximate posterior probabilities from a multilayer perceptron classifier aid in fusing the segmentors and making the final defect classification. Numerical results confirm the feasibility of our approach.

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