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Semiconductor Yield Forecasting Using Quadratic-Programming-Based Fuzzy Collaborative Intelligence Approach

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  • Toly Chen
  • Yu-Cheng Wang

Abstract

Several recent studies have proposed fuzzy collaborative forecasting methods for semiconductor yield forecasting. These methods establish nonlinear programming (NLP) models to consider the opinions of experts and generate fuzzy yield forecasts. Such a practice cannot distinguish between the different expert opinions and can not easily find the global optimal solution. In order to solve some problems and to improve the performance of semiconductor yield forecasting, this study proposes a quadratic-programming- (QP-) based fuzzy collaborative intelligence approach.

Suggested Citation

  • Toly Chen & Yu-Cheng Wang, 2013. "Semiconductor Yield Forecasting Using Quadratic-Programming-Based Fuzzy Collaborative Intelligence Approach," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-7, May.
    • Handle: RePEc:hin:jnlmpe:672404
    DOI: 10.1155/2013/672404
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    Cited by:

    1. Tin-Chih Toly Chen & Yu-Cheng Wang & Chin-Hau Huang, 2020. "An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach," Mathematics, MDPI, vol. 8(4), pages 1-19, April.

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