Wafer lot output time prediction with a hybrid artificial neural network

Tin-Chih Chen*, Horng Ren Tsai, Hsin Chieh Wu

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

To further enhance the accuracy of lot output time prediction in a wafer fab (fabrication plant), a hybrid artificial neural network is proposed in this study. At first, the concept of input classification is applied to Chen's fuzzy back propagation network (FBPN) by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the FBPN. Then, examples belonging to different categories are learned with the same FBPN but with different parameter values. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of three existing approaches, FBPN without example classification, case-based reasoning (CBR), and evolving fuzzy rules (EFR), in most cases by achieving a 15%-45% (and an average of 31%) reduction in the root-mean-squared-error (RMSE).

Original languageEnglish
Pages (from-to)817-823
Number of pages7
JournalWSEAS Transactions on Computers
Volume5
Issue number5
StatePublished - 1 May 2006

Keywords

  • Fuzzy back propagation network
  • Output time prediction
  • Self-organization map
  • Wafer fab

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