1 April 2010 New automatic defect classification algorithm based on a classification-after-segmentation framework
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J. of Electronic Imaging, 19(2), 020502 (2010). doi:10.1117/1.3429116
Abstract
We propose a new method that classifies wafer images according to their defect types for automatic defect classification in semiconductor fabrication processes. Conventional image classifiers using global properties cannot be used in this problem, because the defects usually occupy very small regions in the images. Hence, the defects should first be segmented, and the shape of the segment and the features extracted from the region are used for classification. In other words, we need to develop a classification-after-segmentation approach for the use of features from the small regions corresponding to the defects. However, the segmentation of scratch defects is not easy due to the shrinking bias problem when using conventional methods. We propose a new Markov random field-based method for the segmentation of wafer images. Then we design an AdaBoost-based classifier that uses the features extracted from the segmented local regions.
Sang-Hak Lee, Hyung-Il Koo, Nam-Ik Cho, "New automatic defect classification algorithm based on a classification-after-segmentation framework," Journal of Electronic Imaging 19(2), 020502 (1 April 2010). http://dx.doi.org/10.1117/1.3429116
Submission: Received ; Accepted
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KEYWORDS
Image segmentation

Particles

Feature extraction

Semiconducting wafers

Image processing

Image filtering

Image classification

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