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A novel defect detection and identification method in optical inspection.

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Title: A novel defect detection and identification method in optical inspection.
Authors: Xie, Liangjun1 liangjunxie@gmail.com, Huang, Rui2, Gu, Nong3, Cao, Zhiqiang4
Source: Neural Computing & Applications. Jun2014, Vol. 24 Issue 7/8, p1953-1962. 10p.
Subjects: Optical quality control, Point defects, Semiconductor industry, Pattern recognition systems, Support vector machines, Image processing
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. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A novel defect detection and identification method in optical inspection.
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  Data: <searchLink fieldCode="AR" term="%22Xie%2C+Liangjun%22">Xie, Liangjun</searchLink><relatesTo>1</relatesTo><i> liangjunxie@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Rui%22">Huang, Rui</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Gu%2C+Nong%22">Gu, Nong</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Cao%2C+Zhiqiang%22">Cao, Zhiqiang</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jun2014, Vol. 24 Issue 7/8, p1953-1962. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Optical+quality+control%22">Optical quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Point+defects%22">Point defects</searchLink><br /><searchLink fieldCode="DE" term="%22Semiconductor+industry%22">Semiconductor industry</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00521-013-1442-7
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        Text: English
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              Text: Jun2014
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