A hybrid and intelligent system for predicting lot output time in a semiconductor fabrication factory

Tin-Chih Chen*, Yu Cheng Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

Predicting the output time of every lot in a semiconductor fabrication factory (wafer fab) is a critical task to the wafer fab. To further enhance the effectiveness of wafer lot output time prediction, a hybrid and intelligent system is constructed in this study. The system is composed of two major parts (a k-means classifier and a back-propagation-network regression) and has three intelligent features: incorporating the future release plan of the fab (look-ahead), example classification, and artificial neural networking. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the hybrid and intelligent system was significantly better than those of four existing approaches: BPN, case-based reasoning (CBR), FBPN, kM-BPN, by achieving a 9%-44% (and an average of 25%) reduction in the root-mean-squared-error (RMSE) over the comparison basis-BPN.

Original languageEnglish
Title of host publicationRough Sets and Current Trends in Computing - 5th International Conference, RSCTC 2006, Proceedings
PublisherSpringer Verlag
Pages757-766
Number of pages10
ISBN (Print)3540476938, 9783540476931
DOIs
StatePublished - 1 Jan 2006
Event5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006 - Kobe, Japan
Duration: 6 Nov 20068 Nov 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4259 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006
CountryJapan
CityKobe
Period6/11/068/11/06

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