CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
A novel defect detection and identification method in optical inspection
Xie, Liangjun; Huang, Rui1; Gu, Nong2; Cao, Zhiqiang3
Source PublicationNEURAL COMPUTING & APPLICATIONS
2014-06-01
Volume24Issue:7-8Pages:1953-1962
SubtypeArticle
AbstractOptical 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.
KeywordOptical Inspection Defect Detection Classification Support Vector Machine Adaptive Resonance Theory Network
WOS HeadingsScience & Technology ; Technology
WOS KeywordNEURAL-NETWORK APPROACH ; SEMICONDUCTOR FABRICATION ; AUTOMATIC IDENTIFICATION ; PATTERN-RECOGNITION ; SPATIAL-PATTERN ; CLASSIFICATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000336371900043
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3507
Collection复杂系统管理与控制国家重点实验室_先进机器人
Affiliation1.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
Recommended Citation
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.
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