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Journal of Electronic Imaging • Open Access

New automatic defect classification algorithm based on a classification-after-segmentation framework
Author(s): Sang-Hak Lee; Hyung-Il Koo; Nam-Ik Cho

Paper 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.

Paper Details

Date Published: 1 April 2010
PDF: 3 pages
J. Electron. Imaging. 19(2) 020502 doi: 10.1117/1.3429116
Published in: Journal of Electronic Imaging Volume 19, Issue 2
Show Author Affiliations
Sang-Hak Lee, Seoul National Univ. (Korea, Republic of)
Hyung-Il Koo, Seoul National Univ. (Korea, Republic of)
Nam-Ik Cho, Seoul National Univ. (Korea, Republic of)


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