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

SungHo Kim | Zendy; Yun-Jong Han | Zendy; Geum-Dong Bae | Zendy

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

Author(s) -

SungHo Kim,

Yun-Jong Han,

Geum-Dong Bae

Publication year - 2004

Publication title -

international journal of fuzzy logic and intelligent systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.296

H-Index - 9

eISSN - 2093-744X

pISSN - 1598-2645

DOI - 10.5391/ijfis.2004.4.2.172

Subject(s) - filtration (mathematics) , filter (signal processing) , inverse , semiconductor , artificial neural network , yield (engineering) , computer science , fine tuning , electronic engineering , materials science , control theory (sociology) , optoelectronics , engineering , mathematics , artificial intelligence , computer vision , physics , composite material , statistics , geometry , control (management) , quantum mechanics

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.

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