Detection and Classification of Defect Patterns in Optical Inspection Using Support Vector Machines

Optical inspection techniques have been widely used in industry as they are non-destructive, efficient to achieve, easy to process, and can provide rich information on product quality. Defect patterns such as rings, semi-circles, scratches, clusters are the most common defects in the semiconductor industry. Most methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach has been proposed in this paper to detect these defect patterns in noisy images obtained from printed circuit boards, wafers, and etc. A median filter, background removal, morphological operation, segmentation and labeling are employed in the detection stage of our method. Support vector machine (SVM) is used to identify the defect patterns which are resized. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.

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