A hybrid ANN-FIR system for lot output time prediction and achievability evaluation in a wafer fab

Tin-Chih Chen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

A hybrid artificial neural network (ANN)-fuzzy inference rules (FIR) system is constructed in this study for lot output time prediction and achievability evaluation in a fabrication plant (wafer fab), which are critical tasks to the wafer fab. At first, a hybrid and recurrent ANN, i.e. self-organization map (SOM) and fuzzy back propagation network (FBPN), is proposed to predict the output time of a wafer lot. According to experimental results, the prediction accuracy of the hybrid ANN was significantly better than those of some existing approaches. Subsequently, a set of fuzzy inference rules is established to evaluate the achievability of an output time forecast.

Original languageEnglish
Title of host publicationAnalysis and Design of Intelligent Systems using Soft Computing Techniques
EditorsPatricia Melin, Eduardo Gomez Ramirez, Janusz Kacprzyk, Witold Pedrycz
Pages236-245
Number of pages10
DOIs
StatePublished - 1 Dec 2007

Publication series

NameAdvances in Soft Computing
Volume41
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

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  • Cite this

    Chen, T-C. (2007). A hybrid ANN-FIR system for lot output time prediction and achievability evaluation in a wafer fab. In P. Melin, E. Gomez Ramirez, J. Kacprzyk, & W. Pedrycz (Eds.), Analysis and Design of Intelligent Systems using Soft Computing Techniques (pp. 236-245). (Advances in Soft Computing; Vol. 41). https://doi.org/10.1007/978-3-540-72432-2_24