基于时间约束集的集束型设备群调度方法

刘明祥; 周炳海

doi:10.3724/SP.J.1004.2012.00479

基于时间约束集的集束型设备群调度方法

doi: 10.3724/SP.J.1004.2012.00479

刘明祥,
周炳海^,

1.
同济大学机械工程学院上海 201804

详细信息

通讯作者:
周炳海, 同济大学教授.主要研究方向为离散系统(制造系统/物流系统)规划、调度、建模、仿真与控制,精益生产管理及设备预防性维护建模. E-mail: bhzhou@tongji.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2011-06-17
- 修回日期: 2011-09-22
- 刊出日期: 2012-03-20

Scheduling Algorithm of Multi-cluster Tools Based on Time Constraint Sets

LIU Ming-Xiang,
ZHOU Bing-Hai^,

1.
School of Mechanical Engineering, Tongji University, Shanghai 201804

摘要

摘要: 随着300mm晶圆的加工技术问世,工业界开始采用一种全新的晶圆制造设备——集束型设备群(Multi-cluster tools).对于单个集束型设备(Single-cluster tools)调度研究已比较成熟,并提出了多种调度方法,然而对于集束型设备群调度研究尚处在一个起步阶段. 本文对带有驻留约束且具有多种晶圆类型的集束型设备群的调度问题进行了研究,在引入时间约束集概念的基础上建立了调度模型, 同时,提出了一种逐级回溯的调度方法，并对调度算法进行了仿真实验分析. 仿真结果表明本文提出的算法是有效且可行的.
- 集束型设备群 /
- 驻留约束 /
- 时间约束集 /
- 调度
Abstract: Multi-cluster tools have been used as semiconductor manufacturing equipment since the 300mm diameter wafers appeared. While the throughput analysis and scheduling problems of single-cluster tools have been well-studied, the related researches on multi-cluster tools are still at an early stage. Scheduling problems of multi-cluster tools are analyzed with residency constraints and multi-type wafers in this paper. Firstly, the mathematical model of the problems is constructed on the basis of time constraint sets. Then, a step-back tracking scheduling algorithm is put forward for scheduling multi-cluster tools. Finally, experiments are designed to evaluate the proposed algorithm. The results indicate that the proposed algorithm is feasible and valid.
- Multi-cluster tools /
- residency constraints /
- time constraint set /
- scheduling

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参考文献(1)

[1]

Perkinson T L, McLarty P K, Gyurcsik R S, Cavin R K. Single-wafer cluster tool performance: an analysis of throughput. IEEE Transactions on Semiconductor Manufacturing, 1994, 7(3): 369-373[2] Venkatesh S, Davenport R, Foxhoven P, Nulman J. A steady-state throughput analysis of cluster tools: dual-blade versus single-blade robots. IEEE Transactions on Semiconductor Manufacturing, 1997, 10(4): 418-424[3] Rostami S, Hamidzadeh B. Optimal scheduling techniques for cluster tools with process-module and transport-module residency constraints. IEEE Transactions on Semiconductor Manufacturing, 2002, 15(3): 341-349[4] Rostami S, Hamidzadeh B. An optimal residency-aware scheduling technique for cluster tools with buffer module. IEEE Transactions on Semiconductor Manufacturing, 2004, 17(1): 68-73[5] Yoon H J, Lee D Y. Online scheduling of integrated single-wafer processing tools with temporal constraints. IEEE Transactions on Semiconductor Manufacturing, 2005, 18(3): 390-398[6] Ding S, Yi J, Zhang M T. Multicluster tools scheduling: an integrated event graph and network model approach. IEEE Transactions on Semiconductor Manufacturing, 2006, 19(3): 339-351[7] Yi J, Ding S, Song D, Zhang M T. Steady-state throughput and scheduling analysis of multi-cluster tools: a decomposition approach. IEEE Transactions on Automation Science and Engineering, 2008, 5(2): 321-336[8] Chan W K, Yi J, Ding S. On the optimality of one-unit cycle scheduling of multi-cluster tools with single-blade robots. In: Proceedings of the IEEE Conference on Automation Science and Engineering. Scottsdale, USA: IEEE, 2007. 392-397[9] Chan W K, Yi J, Ding S, Song D. Optimal scheduling of k-unit production of cluster tools with single-blade robots. In: Proceedings of the IEEE International Conference on Automation Science and Engineering. Arlington, USA: IEEE, 2008. 335-340[10] Wu N, Chu C, Chu F, Zhou M C. Modeling and schedulability analysis of single-arm cluster tools with wafer residency time constraints using Petri net. In: Proceedings of the IEEE International Conference on Networking, Sensing and Control. Sanya, China: IEEE, 2008. 84-89[11] Lee T E, Park S H. An extended event graph with negative places and tokens for time window constraints. IEEE Transactions on Automation Science and Engineering, 2005, 2(4): 319-332[12] Kim J H, Lee H T. Schedulability analysis of time-constrained cluster tools with bounded time variation by an extended Petri Net. IEEE Transactions Automation Science and Engineering, 2008, 5(3): 490-503[13] Dechter R, Meiri I, Pearl J. Temporal constraint networks. Artificial Intelligence, 1991, 49(1-3): 61-95

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