International Journal of Fuzzy Logic and Intelligent Systems

  • 제4권2호
  • /
  • Pages.172-175
  • /
  • 2004
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Application of Neural Inverse Modeling Scheme to Optimal Parameter Tuning of Filter Test Equipment

  • Kim, Sung-Ho (School of Electronics and Information Engineering, College of Engineering, Kunsan National University) ;
  • Han, Yun-Jong (School of Electronics and Information Engineering, College of Engineering, Kunsan National University) ;
  • Bae, Geum-Dong (School of Electronics and Information Engineering, College of Engineering, Kunsan National University)
  • 발행 : 2004.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Generally, the yield rate of semiconductors is the major factor that affects directly the price of semiconductors. For a high yield rate of semiconductors, the air inside clean room is needed to be purified and high efficient filters are used for this. The filter are made of super-fine fiber and certain pinholes can be easily produced on the filter's surface by inadvertent manufacturing. As these pinholes are not easily detected with the bare sight, these pinholes exert a negative impact to filtration performance of the filter. In this research, not only the automatic test equipment for detecting pinholes is proposed, but also inverse modeling scheme based on artificial neural network is applied for tuning of its important parameters.

키워드

참고문헌

  1. Jordan, M.I & Rumelhart, D.E., 'Forward models: supervised learning with a distal teacher', Cognitive Science, Vol.16, pp307-354, 1992 https://doi.org/10.1016/0364-0213(92)90036-T
  2. Michael A. Casey, 'Understanding musical sound with Forward models and Physical models', Connection Sci., Vol.6, pp.355-371, 1994 https://doi.org/10.1080/09540099408915730
  3. Jang, J.S.R and Sun, C.T., 'Functional equivalence between radial basic function network and fuzzy inference systems', IEEE Trans, Neural Networks, Vol.4, No.1, pp.156-159, 1993 https://doi.org/10.1109/72.182710
  4. J. Moody and C. Darken. Fast learning in networks of locally-tuned processing units. Neural Computation, Vol.1, pp. 281-294,1989 https://doi.org/10.1162/neco.1989.1.2.281
  5. J. Moody and C. Darken. Learning with localized receptive fields. In D. Touretzky, G.Hinton, and T. Sejnowski, editors, Proc. of the 1988 Connectionist Models Summer School. Carnegie Mellon University, Morgan Kaufinann Publishers, 1988