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A novel defect detection and identification method in optical inspection

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Abstract

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.

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Authors and Affiliations

  1. Houston, TX, 77064, USA

    Liangjun Xie

  2. Nec Laboratories China, Beijing, 100084, China

    Rui Huang

  3. Centre for Intelligent Systems Research, Deakin University, 75 Pigdons Road, Waurn Ponds, VIC, 3216, Australia

    Nong Gu

  4. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Zhiqiang Cao

Authors
  1. Liangjun Xie
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  2. Rui Huang
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  3. Nong Gu
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  4. Zhiqiang Cao
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Correspondence to Liangjun Xie.

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Xie, L., Huang, R., Gu, N. et al. A novel defect detection and identification method in optical inspection. Neural Comput & Applic 24, 1953–1962 (2014). https://doi.org/10.1007/s00521-013-1442-7

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  • DOI: https://doi.org/10.1007/s00521-013-1442-7

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