Applying an intelligent neural system to predicting lot output time in a semiconductor fabrication factory

Tin-Chih Chen*

*Corresponding author for this work

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

3 Scopus citations

Abstract

Output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, the concept of input classification is applied to Chen's fuzzy back propagation network (FBPN) approach in this study by pre-classifying input examples with the k-means (kM) classifier before they are fed into the FBPN. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the intelligent neural system was significantly better than those of four existing approaches: BPN, case-based reasoning (CBR), FBPN without example classification, and evolving fuzzy rules (EFR), in most cases by achieving a 11%∼46% (and an average of 31 %) reduction in the root-mean-squared-error (RMSE) over the comparison basis - BPN.

Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Pages581-588
Number of pages8
ISBN (Print)3540464840, 9783540464846
DOIs
StatePublished - 1 Jan 2006
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
Duration: 3 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4234 LNCS - III
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Neural Information Processing, ICONIP 2006
Country/TerritoryChina
CityHong Kong
Period3/10/066/10/06

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