New automatic defect classification algorithm based on a classification-after-segmentation framework

Sang-Hak Lee; Hyung-Il Koo; Nam-Ik Cho

doi:10.1117/1.3429116

1 April 2010 New automatic defect classification algorithm based on a classification-after-segmentation framework

Sang-Hak Lee, Hyung-Il Koo, Nam-Ik Cho

Author Affiliations +

Journal of Electronic Imaging, Vol. 19, Issue 2, 020502 (April 2010). https://doi.org/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.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Sang-Hak Lee, Hyung-Il Koo, and 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). https://doi.org/10.1117/1.3429116

Published: 1 April 2010

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KEYWORDS

Image segmentation

Particles

Feature extraction

Semiconducting wafers

Image processing

Image classification

Image filtering

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