A novel defect detection and identification method in optical inspection

	A novel defect detection and identification method in optical inspection
	Xie, Liangjun ; Huang, Rui 1; Gu, Nong 2; Cao, Zhiqiang3
发表期刊	NEURAL COMPUTING & APPLICATIONS
	2014-06-01
卷号	24 期号:7-8 页码:1953-1962
文章类型	Article
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.
关键词	Optical Inspection Defect Detection Classification Support Vector Machine Adaptive Resonance Theory Network
WOS标题词	Science & Technology ; Technology
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研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000336371900043
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3507
专题	复杂系统认知与决策实验室_先进机器人
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.