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A novel defect detection and identification method in optical inspection

Xie, Liangjun, Huang, Rui, Gu, Nong and Cao, Zhiqiang 2014, A novel defect detection and identification method in optical inspection, Neural computing and applications, vol. 24, no. 7-8, pp. 1953-1962, doi: 10.1007/s00521-013-1442-7.

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Title A novel defect detection and identification method in optical inspection
Author(s) Xie, Liangjun
Huang, Rui
Gu, Nong
Cao, Zhiqiang
Journal name Neural computing and applications
Volume number 24
Issue number 7-8
Start page 1953
End page 1962
Total pages 10
Publisher Springer
Place of publication Berlin, Germany
Publication date 2014-06
ISSN 0941-0643
1433-3058
Keyword(s) adaptive resonance theory network
classification
defect detection
optical inspection
support vector machine
Summary Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional 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 is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers' training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.
Language eng
DOI 10.1007/s00521-013-1442-7
Field of Research 090399 Biomedical Engineering not elsewhere classified
Socio Economic Objective 920111 Nervous System and Disorders
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30055386

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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