Predicting job completion time in a wafer fab with a recurrent hybrid neural network

Tin-Chih Chen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Predicting the completion time of a job is a critical task to a wafer fabrication plant (wafer fab). Many recent studies have shown that pre-classifying a job before predicting the completion time was beneficial to prediction accuracy. However, most classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent prediction approach was suitable for the data was questionable. For tackling these problems, a recurrent hybrid neural network is proposed in this study, in which a job is pre-classified into one category with the k-means (kM) classifier, and then the back propagation network (BPN) tailored to the category is applied to predict the completion time of the job. After that, the prediction error is fed back to the kM classifier to adjust the classification result, and then the completion time of the job is predicted again. After some replications, the prediction accuracy of the hybrid kM-BPN system will be significantly improved.

Original languageEnglish
Title of host publicationAnalysis and Design of Intelligent Systems using Soft Computing Techniques
EditorsPatricia Melin, Eduardo Gomez Ramirez, Janusz Kacprzyk, Witold Pedrycz
Pages226-235
Number of pages10
DOIs
StatePublished - 1 Dec 2007

Publication series

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

Fingerprint Dive into the research topics of 'Predicting job completion time in a wafer fab with a recurrent hybrid neural network'. Together they form a unique fingerprint.

  • Cite this

    Chen, T-C. (2007). Predicting job completion time in a wafer fab with a recurrent hybrid neural network. In P. Melin, E. Gomez Ramirez, J. Kacprzyk, & W. Pedrycz (Eds.), Analysis and Design of Intelligent Systems using Soft Computing Techniques (pp. 226-235). (Advances in Soft Computing; Vol. 41). https://doi.org/10.1007/978-3-540-72432-2_23