Estimating job cycle time in semiconductor manufacturing with an ANN approach equally dividing and post-classifying jobs

Tin-Chih Chen*, Yi Chi Wang, Yu Cheng Lin, Kai Hsiang Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Estimating the cycle time of every job in a semiconductor manufacturing factory is a critical task to the factory. Many recent studies have shown that pre-classifying a job before estimating the cycle time of the job was beneficial to the forecasting accuracy. However, most pre-classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent forecasting approach was suitable for the data was questionable. For tackling these problems, an artificial neural network (ANN) approach that equally divides and post-classifies jobs is proposed in this study in which a job is post-classified by a BPN instead after the forecasting error is generated. In this novel way, only jobs which cycle time forecasts are the same accurate will be clustered into the same category, and the classification algorithm becomes tailored to the forecasting approach. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, some data were collected from an actual semiconductor manufacturing factory. According to experimental results, the forecasting accuracy (measured with root mean squared error (RMSE)) of the proposed methodology was significantly better than those of the other approaches in most cases by achieving a 16%-44% (and an average of 29%) reduction in RMSE over the comparison basis - multiple-factor linear combination (MFLC). The effect of post-classification was also evident.

Original languageEnglish
Title of host publicationAdvanced Manufacture
Subtitle of host publicationFocusing on New and Emerging Technologies - Selected, peer Reviewed Papers from the 2007 International Conference on Advanced Manufacture
PublisherTrans Tech Publications Ltd
Pages469-474
Number of pages6
ISBN (Print)9770255547605
DOIs
StatePublished - 1 Jan 2008
Event2007 SME International Conference on Advanced Manufacture, SME ICAM 2007 - Tainan, Taiwan
Duration: 26 Nov 200828 Nov 2008

Publication series

NameMaterials Science Forum
Volume594
ISSN (Print)0255-5476

Conference

Conference2007 SME International Conference on Advanced Manufacture, SME ICAM 2007
CountryTaiwan
CityTainan
Period26/11/0828/11/08

Keywords

  • Cycle time
  • FBPN
  • Job classification
  • Semiconductor manufacturing

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    Chen, T-C., Wang, Y. C., Lin, Y. C., & Yang, K. H. (2008). Estimating job cycle time in semiconductor manufacturing with an ANN approach equally dividing and post-classifying jobs. In Advanced Manufacture: Focusing on New and Emerging Technologies - Selected, peer Reviewed Papers from the 2007 International Conference on Advanced Manufacture (pp. 469-474). (Materials Science Forum; Vol. 594). Trans Tech Publications Ltd. https://doi.org/10.4028/0-87849-360-3.469