A look-ahead fuzzy back propagation network for lot output time series prediction in a wafer fab

Tin-Chih Chen*

*Corresponding author for this work

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

20 Scopus citations

Abstract

Lot output time series is one of the most important time series data in a wafer fab (fabrication plant). Predicting the output time of every lot is there-fore a critical task to the wafer fab. To further enhance the effectives and efficiency of wafer lot output time prediction, a look-ahead fuzzy back propagation network (FBPN) is constructed in this study with two advanced features: the future release plan of the fab is considered (look-ahead); expert opinions are incorporated. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the look-ahead FBPN was significantly better than those of four existing approaches: multiple-factor linear combination (MFLC), BPN, case-based reasoning (CBR), and FBPN without look-ahead, by achieving a 12%∼37% (and an average of 19%) reduction in the root-mean-squaredor (RMSE) over the comparison basis - MFLC.

Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Pages974-982
Number of pages9
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
CountryChina
CityHong Kong
Period3/10/066/10/06

Fingerprint Dive into the research topics of 'A look-ahead fuzzy back propagation network for lot output time series prediction in a wafer fab'. Together they form a unique fingerprint.

Cite this