Skip to main content
Log in

Machine Allocation in Semiconductor Wafer Fabrication Systems: A Simulation-Based Approach

  • Published:
Journal of Systems Science and Systems Engineering Aims and scope Submit manuscript

Abstract

The problem of maximizing the throughput of Semiconductor Wafer Fabrication Systems is addressed. We model the fabrication systems as a Stochastic Timed Automata and design a discrete-event simulation scheme. The simulation scheme is explicit, fast and achieves high fidelity which captures the feature of reentrant process flow and is flexible to accommodate diversified wafer lot scheduling policies. A series of Marginal Machine Allocation Algorithms are proposed to sequentially allocate machines. Numerical experiments suggest the designed methods are efficient to find good allocation solutions.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data Availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

References

  • Brown S M, Hanschke T, Meents I, Wheeler B R, Zisgen H (2010). Queueing model improves IBM’s semiconductor capacity and lead-time management. INFORMS Journal on Applied Analytics 40(5): 397–407.

    Article  Google Scholar 

  • Burton G (2017). TSMC says 3nm plant could cost it more than $20bn. https://web.archive.org/web/20171012043608/https://www.theinquirer.net/inquirer/news/3018890/tsmc-says-3nm-plant-could-cost-it-more-than-usd20bn, accessed on May 23, 2022.

  • Cassandras C G, Lafortune S (2008). Introduction to Discrete Event Systems(2ed). Springer, New York.

    Book  MATH  Google Scholar 

  • Çatay B, Erengüç Ş S, Vakharia A J (2003). Tool capacity planning in semiconductor manufacturing. Computers & Operations Research 30(9): 1349–1366.

    Article  MATH  Google Scholar 

  • Chen T (2012). Intelligent scheduling approaches for a wafer fabrication factory. Journal of Intelligent Manufacturing 23(3): 897–911.

    Article  Google Scholar 

  • Cigolini R, Franceschetto S, Sianesi A (2022). Shop floor control in the VLSI circuit manufacturing: A simulation approach and a case study. International Journal of Production Research 60(18): 5450–5467.

    Article  Google Scholar 

  • Connors D P, Feigin G E, Yao D D (1996). A queueing network model for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 9(3): 412–427.

    Article  Google Scholar 

  • Crist K, Uzsoy R (2011). Prioritising production and engineering lots in wafer fabrication facilities: A simulation study. International Journal of Production Research 49(11): 3105–3125.

    Article  Google Scholar 

  • Fowler J W, Mönch L, Ponsignon T (2015). Discrete-event simulation for semiconductor wafer fabrication facilities: A tutorial. International Journal of Industrial Engineering 22(5): 661–682.

    Google Scholar 

  • Geng N, Jiang Z (2009). A review on strategic capacity planning for the semiconductor manufacturing industry. International Journal of Production Research 47(13): 3639–3655.

    Article  MATH  Google Scholar 

  • Geng N, Jiang Z, Chen F (2009). Stochastic programming based capacity planning for semiconductor wafer fab with uncertain demand and capacity. European Journal of Operational Research 198(3): 899–908.

    Article  MATH  Google Scholar 

  • Ghasemi A, Azzouz R, Laipple G, Kabak, K E, Heavey C (2020). Optimizing capacity allocation in semiconductor manufacturing photolithography area—case study: Robert bosch. Journal of Manufacturing Systems 54: 123–137.

    Article  Google Scholar 

  • Goodwin T, Xu J, Celik N, Chen C H (2022). Realtime digital twin-based optimization with predictive simulation learning. Journal of Simulation. DOI: https://doi.org/10.1080/17477778.2022.2046520.

  • Hsieh B W, Chen C H, Chang S C (2007). Efficient simulation-based composition of scheduling policies by integrating ordinal optimization with design of experiment. IEEE Transactions on Automation Science and Engineering 4(4): 553–568.

    Article  Google Scholar 

  • Hsieh L Y, Chang K H, Chien C F (2014). Efficient development of cycle time response surfaces using progressive simulation metamodeling. International Journal of Production Research 52(10): 3097–3109.

    Article  Google Scholar 

  • Kopp D, Hassoun M, Kalir A, Mönch L (2020). SMT2020 — A semiconductor manufacturing testbed. IEEE Transactions on Semiconductor Manufacturing 33(4): 522–531.

    Article  Google Scholar 

  • Kumar P, Meyn S P (1995). Stability of queueing networks and scheduling policies. IEEE Transactions on Automatic Control 40(2): 251–260.

    Article  MathSciNet  MATH  Google Scholar 

  • Kumar S, Kumar P (2001). Queueing network models in the design and analysis of semiconductor wafer fabs. IEEE Transactions on Robotics and Automation 17(5): 548–561.

    Article  Google Scholar 

  • Liu M (2005). The advanced foundry in the consumer electronics era. Keynote Presentation at 2nd ISMI Symposium on Manufacturing Effectiveness, USA.

  • Lu S C, Ramaswamy D, Kumar P (1994). Efficient scheduling policies to reduce mean and variance of cycle-time in semiconductor manufacturing plants. IEEE Transactions on Semiconductor Manufacturing 7(3): 374–388.

    Article  Google Scholar 

  • Morgan J (2022). Supply chain issues and autos: When will the chip shortage end? https://www.jpmorgan.com/insights/research/supply-chain-chip-shortage, accessed on Jan 02, 2023.

  • Peng Y, Xu J, Lee L H, Hu J, Chen C H (2018). Efficient simulation sampling allocation using multifidelity models. IEEE Transactions on Automatic Control 64(8): 3156–3169.

    Article  MathSciNet  MATH  Google Scholar 

  • Perkins J R, Humes C, Kumar P (1994). Distributed scheduling of flexible manufacturing systems: Stability and performance. IEEE Transactions on Robotics and Automation 10(2): 133–141.

    Article  Google Scholar 

  • Shanthikumar J G, Yao D D (1988). On server allocation in multiple center manufacturing systems. Operations Research 36(2): 333–342.

    Article  MathSciNet  MATH  Google Scholar 

  • Weber R R (1980). Note — On the marginal benefit of adding servers to g/gi/m queues. Management Science 26(9): 946–951.

    Article  MathSciNet  MATH  Google Scholar 

  • Wu Y (2023). A cpp implementation of SWFS simulation. https://github.com/xmlongan/SWFS.git, accessed on Jan 4, 2023.

  • Wu Y, Chong I G (2017). Machine allocation in a semiconductor wafer fabrication system. 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference, China.

  • Yang F, Ankenman B, Nelson B L (2007). Efficient generation of cycle time-throughput curves through simulation and metamodeling. Naval Research Logistics 54(1): 78–93.

    Article  MathSciNet  MATH  Google Scholar 

  • Yang F, Ankenman B E, Nelson B L (2008). Estimating cycle time percentile curves for manufacturing systems via simulation. INFORMS Journal on Computing 20(4): 628–643.

    Article  Google Scholar 

  • Yeong-Dae K, Dong-Ho L, Jung-Ug K, Hwan-Kyun R (1998). A simulation study on lot release control, mask scheduling, and batch scheduling in semiconductor wafer fabrication facilities. Journal of Manufacturing Systems 17(2): 107–117.

    Article  Google Scholar 

  • Zhang F, Song J, Dai Y, Xu J (2020). Semiconductor wafer fabrication production planning using multi-fidelity simulation optimisation. International Journal of Production Research 58(21): 6585–6600.

    Article  Google Scholar 

  • Zhang Z, Guan Z, Gong Y, Shen Q (2021). Multi-fidelity simulation-based optimisation for large-scale production release planning in wafer fabs. IFIP International Conference on Advances in Production Management Systems, France.

  • Zhang Z, Guan Z, Gong Y, Luo D, Yue L (2022). Improved multi-fidelity simulation-based optimisation: Application in a digital twin shop floor. International Journal of Production Research 60(3): 1016–1035.

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in partial by the National Natural Science Foundation of China (NSFC) under Grant No. U2268209. The authors thank the editor and three anonymous reviewers for their comments and suggestions which help to improve the article greatly.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanfeng Wu.

Additional information

Yanfeng Wu is an assistant professor at the School of Finance, Jiangxi University of Finance and Economics. He received his Ph.D. from Fudan University. His research interests include discrete-event stochastic systems, stochastic optimization and statistical inference.

Sihua Chen is a professor at the School of Information Management, Jiangxi University of Finance and Economics. His research interests include human-computer intersection, hybrid intelligence, e-commerce, business intelligence, big data analysis and application.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Chen, S. Machine Allocation in Semiconductor Wafer Fabrication Systems: A Simulation-Based Approach. J. Syst. Sci. Syst. Eng. 32, 372–390 (2023). https://doi.org/10.1007/s11518-023-5558-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11518-023-5558-8

Keywords

Navigation