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Empirical Evaluation of a Queueing Network Model for Semiconductor Wafer Fabrication

Author

Listed:
  • Hong Chen

    (Stanford University, Stanford, California)

  • J. Michael Harrison

    (Stanford University, Stanford, California)

  • Avi Mandelbaum

    (Stanford University, Stanford, California)

  • Ann Van Ackere

    (Stanford University, Stanford, California)

  • Lawrence M. Wein

    (Stanford University, Stanford, California)

Abstract

This paper concerns performance modeling of semiconductor manufacturing operations. More specifically, it focuses on queueing network models for an analysis of wafer fabrication facilities. The congestion problems that plague wafer fabrication facilities are described in general terms, and several years' operating data from one particular facility are summarized. A simple queueing network model of that facility is constructed, and the model is used to predict certain key system performance measures. The values predicted by the model are found to be within about 10% of those actually observed. These results suggest that queueing network models can provide useful quantitative guidance to designers of wafer fabrication facilities, and we discuss refinements and extensions of our elementary model that are likely to be important in other settings. However, an even more important benefit to be gained from queueing theory is the simple qualitative point that congestion and delay in wafer fabrication are caused by variability in the operating environment. To significantly reduce manufacturing cycle times, one must reduce that variability.

Suggested Citation

  • Hong Chen & J. Michael Harrison & Avi Mandelbaum & Ann Van Ackere & Lawrence M. Wein, 1988. "Empirical Evaluation of a Queueing Network Model for Semiconductor Wafer Fabrication," Operations Research, INFORMS, vol. 36(2), pages 202-215, April.
    • Handle: RePEc:inm:oropre:v:36:y:1988:i:2:p:202-215
    DOI: 10.1287/opre.36.2.202
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    Citations

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    Cited by:

    1. Kim, Ilhyung & Tang, Christopher S., 1997. "Lead time and response time in a pull production control system," European Journal of Operational Research, Elsevier, vol. 101(3), pages 474-485, September.
    2. Emmett J. Lodree & Lauren B. Davis, 2016. "Empirical analysis of volunteer convergence following the 2011 tornado disaster in Tuscaloosa, Alabama," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(2), pages 1109-1135, November.
    3. David D. Yao & Shaohui Zheng, 1999. "Sequential Inspection Under Capacity Constraints," Operations Research, INFORMS, vol. 47(3), pages 410-421, June.
    4. Kuang Xu & Yuan Zhong, 2020. "Information and Memory in Dynamic Resource Allocation," Operations Research, INFORMS, vol. 68(6), pages 1698-1715, November.
    5. Toly Chen & Yu-Cheng Wang, 2014. "Enhancing the Effectiveness of Cycle Time Estimation in Wafer Fabrication-Efficient Methodology and Managerial Implications," Sustainability, MDPI, vol. 6(8), pages 1-22, August.
    6. Subba Rao, S. & Gunasekaran, A. & Goyal, S. K. & Martikainen, T., 1998. "Waiting line model applications in manufacturing," International Journal of Production Economics, Elsevier, vol. 54(1), pages 1-28, January.
    7. Yang, Feng, 2010. "Neural network metamodeling for cycle time-throughput profiles in manufacturing," European Journal of Operational Research, Elsevier, vol. 205(1), pages 172-185, August.
    8. Dieter Armbruster & Daniel E. Marthaler & Christian Ringhofer & Karl Kempf & Tae-Chang Jo, 2006. "A Continuum Model for a Re-entrant Factory," Operations Research, INFORMS, vol. 54(5), pages 933-950, October.
    9. Kuroda, M. & Kawada, A., 1995. "Adaptive input control for job-shop type production systems with varying demands using inverse queueing network analysis," International Journal of Production Economics, Elsevier, vol. 41(1-3), pages 217-225, October.
    10. Sandeep Jain & N. Raghavan, 2009. "A queuing approach for inventory planning with batch ordering in multi-echelon supply chains," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(1), pages 95-110, March.
    11. J. G. Dai & Wuqin Lin, 2005. "Maximum Pressure Policies in Stochastic Processing Networks," Operations Research, INFORMS, vol. 53(2), pages 197-218, April.
    12. Lin, Yu-Hsin & Lee, Ching-En, 2001. "A total standard WIP estimation method for wafer fabrication," European Journal of Operational Research, Elsevier, vol. 131(1), pages 78-94, May.

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