A PCA-FBPN approach for job cycle time estimation in a wafer fabrication factory

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Variable replacement is a well-known technique to improve the forecasting performance, but has not been applied to the job cycle time forecasting, which is a critical task to a semiconductor manufacturer. To this end, in this study, principal component analysis (PCA) is applied to enhance the forecasting performance of the fuzzy back propagation network (FBPN) approach. First, to replace the original variables, PCA is applied to form variables that are independent of each other, and become new inputs to the FBPN. Subsequently, a FBPN is constructed to estimate the cycle times of jobs. According to the results of a case study, the hybrid PCA-FBPN approach was more efficient, while achieving a satisfactory estimation performance.

Original languageEnglish
Pages (from-to)50-67
Number of pages18
JournalInternational Journal of Fuzzy System Applications
Volume2
Issue number2
DOIs
StatePublished - 1 Jan 2012

Keywords

  • Cycle Time
  • Estimation
  • Fuzzy Back Propagation Network (FBPN)
  • Principal Component Analysis (PCA)
  • Wafer Fabrication

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