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Fault Detection of Reactive Ion Etching Using Time Series Neural Networks

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Maximizing the productivity in semiconductor manufacturing, early detection of process and/or equipment abnormality. Since most of the key processes in semiconductor production are performed under extremely high vacuum condition, no other action can be taken unless the undergoing process is terminated. In this paper, time series based neural networks have been employed to assist the decision for determining potential process fault in real-time. Principal component analysis (PCA) for the dimensionality reduction of the data is first performed to handle smoothly in real-time environment. According to the PCA, 11 system parameters were selected, and each of them were then classified using modeled and tested in time series. Successful detection on different types of process shift (or fault) was achieved with 0% false alarm.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Myongji University, Yongin, Korea

    Kyung-Han Ryu & Jaehyun Park

  2. Department of Information Engineering, Myongji University, Yongin, Korea

    Song-Jae Lee & Dong-Chul Park

  3. Department of Electronic Engineering & Nano-Bio Research Center, Myongji University, Yongin, Korea

    Sang J. Hong

Authors
  1. Kyung-Han Ryu
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  2. Song-Jae Lee
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  3. Jaehyun Park
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  4. Dong-Chul Park
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  5. Sang J. Hong
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Ryu, KH., Lee, SJ., Park, J., Park, DC., Hong, S.J. (2006). Fault Detection of Reactive Ion Etching Using Time Series Neural Networks. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_56

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  • DOI: https://doi.org/10.1007/11760191_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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