An innovative fuzzy and artificial neural network approach for forecasting yield under an uncertain learning environment

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Most methods for fitting an uncertain yield learning process involve using fuzzy logic and solving mathematical programming (MP) problems, and thus have several drawbacks. The present study proposed a novel fuzzy and artificial neural network (ANN) approach for overcoming these drawbacks. In the proposed methodology, an ANN is used instead of an MP model to facilitate generating feasible solutions. A two-stage procedure is established to train the ANN. The proposed methodology and several existing methods were applied to a real case in a semiconductor manufacturing factory, and the experimental results showed that the methodology outperformed the existing methods in the overall forecasting performance.

Original languageEnglish
Pages (from-to)1013-1025
Number of pages13
JournalJournal of Ambient Intelligence and Humanized Computing
Volume9
Issue number4
DOIs
StatePublished - 1 Aug 2018

Keywords

  • Artificial neural network
  • Fuzzy
  • Learning
  • Semiconductor
  • Yield

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