Automatic defect classification for semiconductor manufacturing |
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Authors: | Paul B Chou A Ravishankar Rao Martin C Sturzenbecker Frederick Y Wu Virginia H Brecher |
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Affiliation: | (1) I.B.M., T. J. Watson Research Center, Yorktown Heights, NY 10598, USA, US |
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Abstract: | Visual defect inspection and classification are important parts of most manufacturing processes in the semiconductor and electronics
industries. Defect classification provides relevant information to correct process problems, thereby enhancing the yield and
quality of the product. This paper describes an automated defect classification (ADC) system that classifies defects on semiconductor
chips at various manufacturing steps. The ADC system uses a golden template method for defect re-detection, and measures several
features of the defect, such as size, shape, location and color. A rule-based system classifies the defects into pre-defined
categories that are learnt from training samples. The system has been deployed in the IBM Burlington 16 M DRAM manufacturing
line for more than a year. The system has examined over 100 000 defects, and has met the design criteria of over 80% classification
rate and 80% classification accuracy. Issues involving system design tradeoff, implementation, performance, and deployment
are closely examined. |
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Keywords: | :Machine vision – Process control – Defects – Classification – Semiconductor manufacturing |
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