Skip to main content
Springer Nature Link
Log in

A Hybrid and Intelligent System for Predicting Lot Output Time in a Semiconductor Fabrication Factory

  • Conference paper
Rough Sets and Current Trends in Computing (RSCTC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4259))

Included in the following conference series:

Abstract

Predicting the output time of every lot in a semiconductor fabrication factory (wafer fab) is a critical task to the wafer fab. To further enhance the effectiveness of wafer lot output time prediction, a hybrid and intelligent system is constructed in this study. The system is composed of two major parts (a k-means classifier and a back-propagation-network regression) and has three intelligent features: incorporating the future release plan of the fab (look-ahead), example classification, and artificial neural networking. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the hybrid and intelligent system was significantly better than those of four existing approaches: BPN, case-based reasoning (CBR), FBPN, kM-BPN, by achieving a 9%~44% (and an average of 25%) reduction in the root-mean-squared-error (RMSE) over the comparison basis – BPN.

This work was support by the National Science Council, R.O.C.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Barman, S.: The Impact of Priority Rule Combinations on Lateness and Tardiness. IIE Transactions 30, 495–504 (1998)

    Article  Google Scholar 

  2. Chang, P.-C., Hsieh, J.-C.: A Neural Networks Approach for Due-date Assignment in a Wafer Fabrication Factory. International Journal of Industrial Engineering 10(1), 55–61 (2003)

    MathSciNet  Google Scholar 

  3. Chang, P.-C., Hsieh, J.-C., Liao, T.W.: A Case-Based Reasoning Approach for Due-Date Assignment in a Wafer Fabrication Factory. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS, vol. 2080, p. 648. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  4. Chang, P.-C., Hsieh, J.-C., Liao, T.W.: Evolving Fuzzy Rules for Due-date Assignment Problem in Semiconductor Manufacturing Factory. Journal of Intelligent Manufacturing 16, 549–557 (2005)

    Article  Google Scholar 

  5. Chen, T.: A Fuzzy Back Propagation Network for Output Time Prediction in a Wafer Fab. Journal of Applied Soft Computing 2/3F, 211–222 (2003)

    Google Scholar 

  6. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  7. Lin, C.-Y.: Shop Floor Scheduling of Semiconductor Wafer Fabrication Using Real-time Feedback Control and Prediction. Ph.D. Dissertation, Engineering-Industrial Engineering and Operations Research, University of California at Berkeley (1996)

    Google Scholar 

  8. Vig, M.M., Dooley, K.J.: Dynamic Rules for Due-date Assignment. International Journal of Production Research 29(7), 1361–1377 (1991)

    Article  Google Scholar 

  9. Wang, L.-X., Mendel, J.M.: Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man, and Cybernetics 22(6), 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Systems Management, Feng Chia University, 100, Wenhwa Road, Seatwen, Taichung City, Taiwan

    Toly Chen

  2. Department of Industrial Engineering and Management, Overseas Chinese Institute of Technology, 100, Chiaokwang Road, Taichung City, Taiwan

    Yu-Cheng Lin

Authors
  1. Toly Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-Cheng Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco

  2. Graduate School of Engineering, Department of Electrical Engineering and Computer Sciences, University of Hyogo, 2167 Shosha, 671-2280,, Himeji, Hyogo, Japan

    Yutaka Hata

  3. Department of Medical Informatics, Faculty of Medicine, Shimane University, 89-1 Enya-cho, Izumo, 693-8501, Shimane, Japan

    Shoji Hirano

  4. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3, Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

  5. Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto

  6. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Hung Son Nguyen

  7. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, T., Lin, YC. (2006). A Hybrid and Intelligent System for Predicting Lot Output Time in a Semiconductor Fabrication Factory. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_78

Download citation

  • DOI: https://doi.org/10.1007/11908029_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47693-1

  • Online ISBN: 978-3-540-49842-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics