DOI QR코드

DOI QR Code

Optimization for robot operations in cluster tools for concurrent manufacturing of multiple wafer types

복수 타입의 웨이퍼 혼류생산을 위한 클러스터 장비 로봇 운영 최적화

  • Tae-Sun Yu (Division of Systems Management and Engineering, Pukyong National University) ;
  • Jun-Ho Lee (School of Business, Chungnam National University) ;
  • Sung-Gil Ko (Division of Energy Resource and Industrial Engineering, Kangwon National University)
  • Received : 2023.12.18
  • Accepted : 2023.12.28
  • Published : 2023.12.31

Abstract

Cluster tools are extensively employed in various wafer fabrication processes within the semiconductor manufacturing industry, including photo lithography, etching, and chemical vapor deposition. Contemporary fabrication facilities encounter customer orders with technical specifications that are similar yet slightly varied. Consequently, modern fabrications concurrently manufacture two or three different wafer types using a cluster tool to maximize chamber utilization and streamline the flow of wafer lots between different process stages. In this review, we introduce two methods of concurrent processing of multiple wafer types: 1) concurrent processing of multiple wafer types with different job flows, 2) concurrent processing of multiple wafer types with identical job flows. We describe relevant research trends and achievements and discuss future research directions.

Keywords

Acknowledgement

이 논문은 국립부경대학교 자율창의학술연구비(2022년)에 의하여 연구되었음.

References

  1. Lee, T. E., Kim, H. J., Yu, T. S., 2023, Semiconductor Manufacturing Automation, Springer Nature, Switzerland.
  2. Lee, T. E., Park, S. H., 2005, An extended event graph with negative places and tokens for time window constraints, IEEE Trans. Autom. Sci. Eng., 2:4 319-332. https://doi.org/10.1109/TASE.2005.851236
  3. Jung, C., Kim, H. J., Lee, T. E., 2015, A branch and bound algorithm for cyclic scheduling of timed Petri nets, IEEE Trans. Autom. Sci. Eng., 12:1 309-323. https://doi.org/10.1109/TASE.2013.2285221
  4. Lee, T. E., Park, S. H., Jung, C., 2014, Steady state analysis of timed event graphs with time window constraints, Discrete Appl. Math. 167 202-216. https://doi.org/10.1016/j.dam.2013.12.006
  5. Lee, J. H., Kim, H. J., Lee, T. E., 2014, Scheduling cluster tools for concurrent processing of two wafer types, IEEE Trans. Autom. Sci. Eng., 11:2 525-536. https://doi.org/10.1109/TASE.2013.2296855
  6. Lee, J. H., Kim, H. J., Lee, T. E., 2015, Scheduling cluster tools for concurrent processing of two wafer types with PM sharing, IEEE Trans. Autom. Sci. Eng., 53:19 6007-6022. https://doi.org/10.1080/00207543.2015.1035813
  7. Ko, S. G., Yu, T. S., Lee, T. E., 2019, Scheduling Dual-Armed Cluster Tools for Concurrent Processing of Multiple Wafer Types With Identical Job Flows, IEEE Trans. Autom. Sci. Eng., 16:3 1058-1070. https://doi.org/10.1109/TASE.2018.2868004
  8. Ko, S. G., Yu, T. S., Lee, T. E., 2021, Wafer Delay Analysis and Workload Balancing of Parallel Chambers for Dual-Armed Cluster Tools With Multiple Wafer Types, IEEE Trans. Autom. Sci. Eng., 18:3 1516-1526. https://doi.org/10.1109/TASE.2021.3061140
  9. Kim, T. K., Jung, C., Lee, T. E., 2012, Scheduling start-up and close-down periods of dual-armed cluster tools with wafer delay regulation, Int. J. Prod. Res 50:10 2785-2795. https://doi.org/10.1080/00207543.2011.590949
  10. Yu, T. S., Kim, H. J., Lee, T. E., 2018, Scheduling single-armed cluster tools with chamber cleaning operations, IEEE Trans. Autom. Sci. Eng., 15:2 705-716. https://doi.org/10.1109/TASE.2017.2682271
  11. Yu, T. S., Lee, T. E., 2020, Wafer delay analysis and control of dual-armed cluster tools with chamber cleaning operations, IEEE Trans. Autom. Sci. Eng., 58:2 434-447. https://doi.org/10.1080/00207543.2019.1593547
  12. Lee, T. E, 2008, A review of scheduling theory and methods for semiconductor manufacturing cluster tools, In: Proceedings of the 2008 Winter Simulation Conf., 2127-2135.
  13. Roh, J. E., Lee, T. E., 2017, A reinforcement learning approach to scheduling dual-armed cluster tools with time variations, In: Proceedings of the 16th Int. Conf. on Modeling and Applied Simulation, 1-6.
  14. Kim, D. Y., Kim, H. J., 2020, Reentrant flow shop scheduling using reinforcement learning, In: Proceedings of 16th IEEE Int. Conf. on Autom. Sci. Eng., 1646-1647.
  15. Kim, H. J., Lee, J. H., Lee, T. E., 2015, Noncyclic scheduling of cluster tools with a branch and bound algorithm, IEEE Trans. Autom. Sci. Eng., 12:2 690-700. https://doi.org/10.1109/TASE.2013.2293552
  16. Pan, C., Zaoh, K., Lu, Y., Zhang, F., 2018, Scheduling and optimization of mixed-processing with multi-variety wafers in dual-armed cluster tools, J. Chin. Inst. Eng., 41:6 463-472. https://doi.org/10.1080/02533839.2018.1516515
  17. Lu, Y., Qiao, Y., Pan, C., Chen, Y., Wu, N., Li, Z., Liu, B., 2021, Modeling and Control for Deadlock-Free Operation of Single-Arm Cluster Tools With Concurrently Processing Multiple Wafer Types via Petri Net, IEEE Access 9 70868-70883. https://doi.org/10.1109/ACCESS.2021.3077503
  18. Wang, J., Pan, C., Hu, H., Li, L., Zhou, Y., 2019, A cyclic scheduling approach to single-arm cluster tools with multiple wafer types and residency time constraints, IEEE Trans. Autom. Sci. Eng., 16:3 1373-1386. https://doi.org/10.1109/TASE.2018.2878063
  19. Fu, J., Pan, C., 2020, Intelligent Scheduling Methods for Challenges of Cluster Tools with Concurrent Processing of Multiple Wafer Types, In: Proceedings of 16th IEEE Int. Conf. on Autom. Sci. Eng., 173-178.
  20. Ko, S. G., 2018, Scheduling Cluster Tools for Concurrent Processing of Multiple Wafer Types with Identical Job Flows, Doctorate Dissertation, Korea Advanced Institute of Science and Technology, Republic of Korea.
  21. Dawande, M., Sriskandarajah, C., Sethi, S., 2002, On throughput maximization in constant travel-time robotic cells, Manuf. Service Oper. Manag., 4:4 296-312. https://doi.org/10.1287/msom.4.4.296.5731
  22. Sriskandarajah, C., Drobouchevitch, I., Sethi, S., Chandrasekaran, R., 2004, Scheduling multiple parts in a robotic cell served by a dual-gripper robot, Oper. Res. 52:1 65-82. https://doi.org/10.1287/opre.1030.0073