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Application of neural networks for automated X-ray image inspection in electronics manufacturing

  • Engeneering Applications
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Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

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Abstract

Artificial neural networks have proven to be valuable tools in industrial problems, e.g. for image processing, and classification in visual inspection tasks. Typically, todays successful systems have a heterogeneous structure, applying small and specialised neural networks together with classical and heuristic methods in a hybrid framework. This paper reports on the practical application of such a system, developed in prior work, which efficiently employs selected neural networks in an innovative framework. In particular, the application in electronics manufacturing with advanced sensor technology was subject of investigation.

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Author information

Authors and Affiliations

  1. Chair of Electronic Devices and Integrated Circuits, University of Technology Dresden, 01062, Dresden, Germany

    Andreas König

  2. Center of Microtechnical Manufacturing (ZμP), University of Technology Dresden, 01062, Dresden, Germany

    Andreas Herenz

  3. Chair of Procedure Technology of Electronics, University of Technology Dresden, 01062, Dresden, Germany

    Klaus Wolter

Authors
  1. Andreas König
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  2. Andreas Herenz
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  3. Klaus Wolter
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Editor information

José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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König, A., Herenz, A., Wolter, K. (1999). Application of neural networks for automated X-ray image inspection in electronics manufacturing. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100526

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  • DOI: https://doi.org/10.1007/BFb0100526

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

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