A fuzzy-neural approach with collaboration mechanisms for semiconductor yield forecasting

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Scopus citations


Yield forecasting is critical to a semiconductor manufacturing factory. To further enhance the effectiveness of semiconductor yield forecasting, a fuzzy-neural approach with collaboration mechanisms is proposed in this study. The proposed methodology is modified from Chen and Lin's approach by incorporating two collaboration mechanisms: favoring mechanism and disfavoring mechanism. The former helps to achieve the consensus among multiple experts to avoid the missing of actual yield, while the latter shrinks the search region to increase the probability of finding out actual yield. To evaluate the effectiveness of the proposed methodology, it was applied to some real cases. According to experimental results, the proposed methodology improved both precision and accuracy of semiconductor yield forecasting by 58% and 35%, respectively.

Original languageEnglish
Pages (from-to)17-33
Number of pages17
JournalInternational Journal of Intelligent Information Technologies
Issue number3
StatePublished - 1 Jul 2010


  • Collaborative
  • Expert system
  • Fuzzy neural
  • Semiconductor
  • Yield Forecasting

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