A novel defect detection and identification method in optical inspection

• Published in: Neural computing & applications 2014-06, Vol.24 (7-8), p.1953-1962
• Main Authors: Xie, Liangjun, Huang, Rui, Gu, Nong, Cao, Zhiqiang
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Cross-disciplinary physics: materials science; rheology; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Exact sciences and technology; Image Processing and Computer Vision; Industrial metrology. Testing; Materials science; Materials testing; Mechanical engineering. Machine design; Memory organisation. Data processing; Original Article; Pattern recognition. Digital image processing. Computational geometry; Physics; Probability and Statistics in Computer Science; Software
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-013-1442-7

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.