A collaborative and artificial intelligence approach for semiconductor cost forecasting

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Scopus citations


Forecasting the unit cost of a semiconductor product is an important task to the manufacturer. However, it is not easy to deal with the uncertainty in the unit cost. In order to effectively forecast the semiconductor unit cost, a collaborative and artificial intelligence approach is proposed in this study. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own fuzzy neural networks to forecast the semiconductor unit cost based on their viewpoints. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, a radial basis function (RBF) network is used. The effectiveness of the proposed methodology is shown with a case study.

Original languageEnglish
Pages (from-to)476-484
Number of pages9
JournalComputers and Industrial Engineering
Issue number2
StatePublished - 1 Jan 2013


  • Artificial intelligence
  • Collaborative
  • Forecasting
  • Fuzzy neural network

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