Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
권호 표지
DOI QR코드

DOI QR Code

A Simulation-based Optimization for Scheduling in a Fab: Comparative Study on Different Sampling Methods

시뮬레이션 기반 반도체 포토공정 스케줄링을 위한 샘플링 대안 비교

  • 윤현정 (부산대학교 산업공학과 산업데이터공학융합전공) ;
  • 한광욱 (부산대학교 산업공학과 산업데이터공학융합전공) ;
  • 강봉권 (부산대학교 산업공학과 산업데이터공학융합전공) ;
  • 홍순도
  • Received : 2023.07.19
  • Accepted : 2023.09.13
  • Published : 2023.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.

반도체 제조라인(FAB)은 복잡하고 불확실한 운영환경에서 작동하는 대규모의 제조시스템 중 하나로 반도체 설비 운영을 담당하는 엔지니어들은 직관적이고 신속한 공정 스케줄링을 위해 가중치 기반 스케줄링을 널리 사용하고 있다. 가중치 기반 스케줄링에서 가중치 결정은 FAB 성능에 큰 영향을 미치므로 엔지니어들은 가중치 최적화를 위하여 시뮬레이션 기반 의사결정을 활용할 수 있다. 그러나 대규모 시뮬레이션은 많은 실험 비용을 요구하기 때문에 효과적인 의사결정을 위해서 신중한 실험설계가 요구된다. 본 연구에서는 적은 시뮬레이션 실행 내에서 효율적인 스케줄링을 도출하기 위해 세 가지 샘플링 대안(i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search (DSS))에 대한 비교연구를 수행하였다. 시뮬레이션 실험을 통해 세 가지 대안이 단일 규칙보다 우수한 성능을 보였고, 그중 GA와 DSS가 최적화를 위한 효과적인 대안이 될 수 있음을 확인하였다.

Keywords

Acknowledgement

이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음

References

  1. Altendorfer, K., Kabelka, B., and Stocher, W. (2007), A new dispatching rule for optimizing machine utilization at a semiconductor test field, 2007 IEEE/SEMI Advanced Semiconductor Manufacturing Conference, 188-193.
  2. Arisha, A., and Young, P. (2004), Intelligent simulation-based lot scheduling of photolithography toolsets in a wafer fabrication facility, Proceedings of the 2004 Winter Simulation Conference, Vol. 2, 1935-1942.
  3. Chen, B. and Matis, T. I. (2013), A flexible dispatching rule for minimizing tardiness in job shop scheduling, International Journal of Production Economics, 141 (1), 360-365. https://doi.org/10.1016/j.ijpe.2012.08.019
  4. Dabbas, R. M., Fowler, J. W., Rollier, D. A., and Mccarville, D. (2003), Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling. International journal of production research, 41(5), 939-961. https://doi.org/10.1080/0020754021000030402
  5. Han, G. G., Kang, B. G., Kim, H. J., Hong, S. (2022), A GA-based Optimization of a Weighted Lot Targeting Rule in a Semiconductor Wafer Fab. Journal of the Korean Institute of Industrial Engineers, 48(5), 477-485. https://doi.org/10.7232/JKIIE.2022.48.5.477
  6. Kim, H., Lim, D. E., and Lee, S. (2020), Deep learning-based dynamic scheduling for semiconductor manufacturing with high uncertainty of automated material handling system capability. IEEE Transactions on Semiconductor Manufacturing, 33(1), 13-22. https://doi.org/10.1109/TSM.2020.2965293
  7. Lee, J. H., Kim, Y., Kim, Y. B., Kim, B. H., Jung, G. H., and Kim, H. J. (2020), A sequential search method of dispatching rules for scheduling of LCD manufacturing systems. IEEE Transactions on Semiconductor Manufacturing, 33(4), 496-503. https://doi.org/10.1109/TSM.2020.3029124
  8. Lee, W.-J., Kim, B.-H., Ko, K., and Shin, H. (2019), Simulation based mul-ti-objective fab scheduling by using reinforcement learning, 2019 Winter Simulation Conference, 2236-2247.
  9. Li, L., Sun, Z., Zhou, M., and Qiao, F. (2012), Adaptive dispatching rule for semiconductor wafer fabrication facility, IEEE Transactions on Automation Science and Engineering, 10(2), 354-364. https://doi.org/10.1109/TASE.2012.2221087
  10. McKay, M. D., Beckman, R. J., and Conover, W. J. (2000), A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 42(1), 55-61. https://doi.org/10.1080/00401706.2000.10485979
  11. Mittler, M., and Schoemig, A. K. (1999), Comparison of dispatching rules for semiconductor manufacturing using large facility models, Proceedings of the 1999 Winter Simulation Conference, 709-713.
  12. Siebert, M., Bartlett, K., Kim, H., Ahmed, S., Lee, J., Nazzal, D., Nemhauser, G., and Sokol, J. (2018), Lot targeting and lot dispatching decision policies for semiconductor manufacturing: optimisation under uncertainty with simulation validation. International Journal of Production Research, 56(1-2), 629-641. https://doi.org/10.1080/00207543.2017.1387679
  13. Stocki, R. (2005), A method to improve design reliability using optimal Latin hypercube sampling, Computer Assisted Mechanics and Engineering Sciences, 12 (4), 393.
  14. Yan, H., Lou, S., Sethi, S., Gardel, A., and Deosthali, P. (1996), Testing the robustness of two-boundary control policies in semiconductor manufacturing, IEEE Transactions on Semiconductor Manufacturing, 9(2), 285-288. https://doi.org/10.1109/66.492825
  15. Zhang, H., Jiang, Z., and Guo, C. (2009), Simulation-based optimization of dispatching rules for semiconductor wafer fabrication system scheduling by the response surface methodology, The International Journal of Advanced Manufacturing Technology, 41, 110-121. https://doi.org/10.1007/s00170-008-1462-0