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

Yanfeng Wu , Sihua Chen

Journal of Systems Science and Systems Engineering ›› 2023, Vol. 32 ›› Issue (3) : 372 -390.

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Journal of Systems Science and Systems Engineering ›› 2023, Vol. 32 ›› Issue (3) : 372 -390. DOI: 10.1007/s11518-023-5558-8
Article

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

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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.

Keywords

Semiconductor wafer fabrication system / machine allocation / discrete-event simulation / marginal machine allocation

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Yanfeng Wu, Sihua Chen. Machine Allocation in Semiconductor Wafer Fabrication Systems: A Simulation-Based Approach. Journal of Systems Science and Systems Engineering, 2023, 32(3): 372-390 DOI:10.1007/s11518-023-5558-8

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References

[1]

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

[2]

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.

[3]

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

[4]

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

[5]

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

[6]

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

[7]

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

[8]

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

[9]

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

[10]

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

[11]

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

[12]

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

[13]

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.

[14]

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

[15]

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

[16]

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

[17]

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

[18]

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

[19]

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

[20]

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

[21]

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.

[22]

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

[23]

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

[24]

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

[25]

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

[26]

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

[27]

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

[28]

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

[29]

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

[30]

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

[31]

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

[32]

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.

[33]

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

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