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Wafer Die Position Detection Using Hierarchical Gray Level Corner Detector

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

In this paper, we will introduce a method for wafer die position detection using corner detector. We present a hierarchical gray level corner detector (HGLCD) to detect die position accurately. HGLCD divides the corner region into many homocentric circles and get corner response and angle about each circle. The experiments have shown that the new corner detector is more accurate and more efficient in its performance than other two popular corner detectors.

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References

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Author information

Authors and Affiliations

  1. Soongsil University, Sangdo 5 dong Dongjak Gu, Seoul, Korea

    Jae Hyung Na & Hae Seok Oh

Authors
  1. Jae Hyung Na
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  2. Hae Seok Oh
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Na, J.H., Oh, H.S. (2004). Wafer Die Position Detection Using Hierarchical Gray Level Corner Detector. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_115

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_115

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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