Semiconductor yield forecasting using quadratic-programming-based fuzzy collaborative intelligence approach

Tin-Chih Chen*, Yu Cheng Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

12 Scopus citations

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.

Original languageEnglish
Article number672404
JournalMathematical Problems in Engineering
Volume2013
DOIs
StatePublished - 28 Jun 2013

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