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A novel defect detection and identification method in optical inspection

文献类型：期刊论文

作者Xie, Liangjun; Huang, Rui1; Gu, Nong2; Cao, Zhiqiang3
刊名NEURAL COMPUTING & APPLICATIONS
出版日期2014-06-01
卷号24期号:7-8页码:1953-1962
关键词Optical inspection Defect detection Classification Support vector machine Adaptive resonance theory network
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence
研究领域[WOS]Computer Science
关键词[WOS]NEURAL-NETWORK APPROACH ; SEMICONDUCTOR FABRICATION ; AUTOMATIC IDENTIFICATION ; PATTERN-RECOGNITION ; SPATIAL-PATTERN ; CLASSIFICATION
收录类别SCI
语种英语
WOS记录号WOS:000336371900043
源URL[http://ir.ia.ac.cn/handle/173211/3507]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
作者单位1.Nec Labs China, Beijing 100084, Peoples R China
2.Deakin Univ, Ctr Intelligent Syst Res, Waurn Ponds, Vic 3216, Australia
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Xie, Liangjun,Huang, Rui,Gu, Nong,et al. A novel defect detection and identification method in optical inspection[J]. NEURAL COMPUTING & APPLICATIONS,2014,24(7-8):1953-1962.
APA Xie, Liangjun,Huang, Rui,Gu, Nong,&Cao, Zhiqiang.(2014).A novel defect detection and identification method in optical inspection.NEURAL COMPUTING & APPLICATIONS,24(7-8),1953-1962.
MLA Xie, Liangjun,et al."A novel defect detection and identification method in optical inspection".NEURAL COMPUTING & APPLICATIONS 24.7-8(2014):1953-1962.

入库方式： OAI收割

来源：自动化研究所

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