Comparison of Knowledge Acquisition Methods for Dynamic Scheduling of Wafer Test Processes with Unpredictable Testing Errors

• Matsuo Tsubasa

  Graduate School of Engineering Science, Osaka University

• Inuiguchi Masahiro

  Graduate School of Engineering Science, Osaka University

• Masunaga Kenichiro

  Renesas Electronics Co.

抄録

The scheduling of semiconductor wafer testing processes may be seen as a resource constraint project scheduling problem (RCPSP), but it includes uncertainties caused by wafer error, human factors, etc. Because uncertainties are not simply quantitative, estimating the range of the parameters is not useful. Considering such uncertainties, finding a good situation-dependent dispatching rule is more suitable than solving an RCPSP under uncertainties. In this paper we apply machine learning approaches to acquiring situation-dependent dispatching rule. We compare obtained rules and examine their effectiveness and usefulness in problems with unpredictable wafer testing errors.

収録刊行物

• Journal of Advanced Computational Intelligence and Intelligent Informatics

  Journal of Advanced Computational Intelligence and Intelligent Informatics 19 (1), 58-66, 2015-01-20

  富士技術出版株式会社

キーワード

• semiconductor manufacturing
• simulation
• genetic-based machine learning

詳細情報

• CRID: 1390001288150806272
• NII論文ID: 130007673238
• NII書誌ID: AA12042502
• DOI: 10.20965/jaciii.2015.p0058
• ISSN: 18838014
• ISSN: 13430130
• NDL書誌ID: 026078173
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I026078173
• Web Site: https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=JACII001900010008
• 本文言語コード: en
• データソース種別:
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  • NDL
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可