Neural network applications in automated optical inspection: state of the art

Hyungsuck Cho; Won Shik Park

doi:10.1117/12.455971

27 November 2002 Neural network applications in automated optical inspection: state of the arts

Hyungsuck Cho, Won Shik Park

Proceedings Volume 4789, Algorithms and Systems for Optical Information Processing VI; (2002) https://doi.org/10.1117/12.455971
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

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.

Citation Download Citation

Hyungsuck Cho and Won Shik Park "Neural network applications in automated optical inspection: state of the arts", Proc. SPIE 4789, Algorithms and Systems for Optical Information Processing VI, (27 November 2002); https://doi.org/10.1117/12.455971

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