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Proceedings Paper

Neural network applications in automated optical inspection: state of the art
Author(s): Hyungsuck Cho; Won Shik Park
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Paper Abstract

Optical inspection techniques have been widely adopted in industrial areas since they provide fast and accurate information on product quality, process status, and machine conditions. The technologies include sensing using vision, laser scattering and imaging, x-ray imaging, and other optical sensing, and data processing for classification and recognition problems. Frequently, data processing tasks are very difficult, which is mainly due to the large volume, the complexity, and the noise of the raw data acquired. Artificial neural networks have been proven to be an effective means to cope with the problems difficult to solve or inefficient to solve by convectional methodologies. This paper presents the applications of neural networks in optical inspection tasks. Among the variety of industrial areas, this paper focuses on the inspection tasks involved in printed circuit board manufacturing processes and semiconductor manufacturing processes, which are the most competing industries in the world today. In this paper, the inspection problems are addressed and the optical techniques together with neural networks to solve such problems are reviewed. The application cases to which neural networks are applied are also presented with their effects.

Paper Details

Date Published: 27 November 2002
PDF: 13 pages
Proc. SPIE 4789, Algorithms and Systems for Optical Information Processing VI, (27 November 2002); doi: 10.1117/12.455971
Show Author Affiliations
Hyungsuck Cho, Korea Advanced Institute of Science and Technology (South Korea)
Won Shik Park, Korea Advanced Institute of Science and Technology (South Korea)

Published in SPIE Proceedings Vol. 4789:
Algorithms and Systems for Optical Information Processing VI
Bahram Javidi; Demetri Psaltis, Editor(s)

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