Defect pattern detection using a new rule-based approach
Automated inspection of semiconductor defect data has become increasingly important over the past several years as a means of quickly understanding and controlling contamination sources and process faults, which impact product yield. To address the issue of too much data and too little time, automat...
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sg-ntu-dr.10356-62092023-03-11T18:08:28Z Defect pattern detection using a new rule-based approach Shankar, N. G Zhong Zhaowei School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Mechatronics Automated inspection of semiconductor defect data has become increasingly important over the past several years as a means of quickly understanding and controlling contamination sources and process faults, which impact product yield. To address the issue of too much data and too little time, automation technologies in defect detection and review are being developed by universities, laboratories, industry, and semiconductor equipment suppliers. In this thesis, a new rule-based approach is proposed to segment defect images. Several segmentation techniques already exist but they often focus on the constraints of a specific application and therefore they lack of generality and flexibility. This limits the use of computer vision in all those tasks where the visual data content and the purpose of the defect analysis are not known a priori. Moreover, the limited generality increases the costs for the design of unsupervised defect image analysis systems. DOCTOR OF PHILOSOPHY (MAE) 2008-09-17T11:09:23Z 2008-09-17T11:09:23Z 2006 2006 Thesis Shankar, N. G. (2006). Defect pattern detection using a new rule-based approach. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/6209 10.32657/10356/6209 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Mechanical engineering::Mechatronics Shankar, N. G Defect pattern detection using a new rule-based approach |
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Automated inspection of semiconductor defect data has become increasingly important over the past several years as a means of quickly understanding and controlling contamination sources and process faults, which impact product yield. To address the issue of too much data and too little time, automation technologies in defect detection and review are being developed by universities, laboratories, industry, and semiconductor equipment suppliers. In this thesis, a new rule-based approach is proposed to segment defect images. Several segmentation techniques already exist but they often focus on the constraints of a specific application and therefore they lack of generality and flexibility. This limits the use of computer vision in all those tasks where the visual data content and the purpose of the defect analysis are not known a priori. Moreover, the limited generality increases the costs for the design of unsupervised defect image analysis systems. |
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Zhong Zhaowei |
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Zhong Zhaowei Shankar, N. G |
format |
Theses and Dissertations |
author |
Shankar, N. G |
author_sort |
Shankar, N. G |
title |
Defect pattern detection using a new rule-based approach |
title_short |
Defect pattern detection using a new rule-based approach |
title_full |
Defect pattern detection using a new rule-based approach |
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Defect pattern detection using a new rule-based approach |
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Defect pattern detection using a new rule-based approach |
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defect pattern detection using a new rule-based approach |
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2008 |
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https://hdl.handle.net/10356/6209 |
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1761781442949414912 |