Noncyclic Scheduling for Timed Discrete-Event Systems With Application to Single-Armed Cluster Tools Using Pareto-Optimal Optimization

Recently, semiconductor manufacturing fabs tend to reduce the wafer lot size, down to just a few. Consequently, the wafer recipe or wafer flow pattern changes frequently. For such problems, it is impossible to apply conventional prevalent cyclic scheduling methods that repeat processing of wafers in an identical cyclic tool operation sequence. We therefore consider the noncyclic scheduling problem of single-armed cluster tools that process wafers with different recipes. Our proposed method is to transforms the problem into a multiobjective problem by considering the ready times of the resources as objectives to minimize. Only feasible states are generated based on the initial state of the system. These feasible states form a multiobjective shortest path problem and give us as an upper bound for the number of states, where , and are the number of different wafer recipes, wafers, and processing chambers. We solve this problem with implicit enumeration by making our scheduling decisions based on the Pareto optimal solutions for each state. The experimental results show that the proposed algorithm can quickly solve large sized problems including ones with arbitrary initial tool states, changing recipes, reentrant wafer Ωows, and parallel chambers. Note to Practitioners-The scheduling method developed in this paper is designed for scheduling robots used in semiconductor production. The method is able to efficiently represent the state of the manufacturing system and uses this to find an optimal schedule to maximize productivity. The same approach can be used for other manufacturing systems with discrete events. The main advantage of this method is that it can fast find an optimal schedule based on the current state of the system. A long-time horizon can be used because of the high efficiency of the algorithm with respect to the number of jobs. However, the method is best suited for manufacturing systems with few buffers and limited degrees of freedom, in other words highly interconnected systems.

[1]  R. S. Gyurcsik,et al.  Single-wafer cluster tool performance: an analysis of the effects of redundant chambers and revisitation sequences on throughput , 1996 .

[2]  Wai Kin Chan,et al.  Optimal Scheduling of Multicluster Tools With Constant Robot Moving Times, Part I: Two-Cluster Analysis , 2011, IEEE Transactions on Automation Science and Engineering.

[3]  Karsten Weihe,et al.  Pareto Shortest Paths is Often Feasible in Practice , 2001, WAE.

[4]  Tae-Eog Lee,et al.  A review of scheduling theory and methods for semiconductor manufacturing cluster tools , 2008, 2008 Winter Simulation Conference.

[5]  E. Martins On a multicriteria shortest path problem , 1984 .

[6]  G. W. Evans,et al.  An Overview of Techniques for Solving Multiobjective Mathematical Programs , 1984 .

[7]  R. S. Gyurcsik,et al.  Single-wafer cluster tool performance: an analysis of throughput , 1994 .

[8]  MengChu Zhou,et al.  Modeling, Analysis and Control of Dual-Arm Cluster Tools With Residency Time Constraint and Activity Time Variation Based on Petri Nets , 2012, IEEE Transactions on Automation Science and Engineering.

[9]  Chihyun Jung,et al.  An Efficient Mixed Integer Programming Model Based on Timed Petri Nets for Diverse Complex Cluster Tool Scheduling Problems , 2012, IEEE Transactions on Semiconductor Manufacturing.

[10]  Pengyu Yan,et al.  A branch and bound algorithm for optimal cyclic scheduling in a robotic cell with processing time windows , 2010 .

[11]  Patrice Perny,et al.  Near Admissible Algorithms for Multiobjective Search , 2008, ECAI.

[12]  Shengwei Ding,et al.  Steady-State Throughput and Scheduling Analysis of Multicluster Tools: A Decomposition Approach , 2008, IEEE Transactions on Automation Science and Engineering.

[13]  Raghavan Srinivasan,et al.  Modeling and performance analysis of cluster tools using Petri nets , 1998 .

[14]  W.M. Zuberek,et al.  Cluster tools with chamber revisiting-modeling and analysis using timed Petri nets , 2004, IEEE Transactions on Semiconductor Manufacturing.

[15]  Chelsea C. White,et al.  Multiobjective A* , 1991, JACM.

[16]  Edward W. Davis,et al.  An Algorithm for Optimal Project Scheduling under Multiple Resource Constraints , 1971 .

[17]  Hyun-Jung Kim,et al.  Scheduling of cluster tools with ready time constraints for small lot production , 2011, 2011 IEEE International Conference on Automation Science and Engineering.

[18]  David S. Johnson,et al.  Computers and Inrracrobiliry: A Guide ro the Theory of NP-Completeness , 1979 .

[19]  H. Neil Geismar,et al.  Throughput optimization in robotic cells with input and output machine buffers: A comparative study of two key models , 2010, Eur. J. Oper. Res..

[20]  Hyun Joong Yoon,et al.  Real-time scheduling of wafer fabrication with multiple product types , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[21]  Tae-Eog Lee,et al.  Scheduling single-armed cluster tools with reentrant wafer flows , 2006 .

[22]  MengChu Zhou,et al.  Schedulability Analysis and Optimal Scheduling of Dual-Arm Cluster Tools With Residency Time Constraint and Activity Time Variation , 2012, IEEE Transactions on Automation Science and Engineering.

[23]  H. Neil Geismar,et al.  Productivity Improvement From Using Machine Buffers in Dual-Gripper Cluster Tools , 2011, IEEE Transactions on Automation Science and Engineering.

[24]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[25]  H. Neil Geismar,et al.  Dominance of Cyclic Solutions and Challenges in the Scheduling of Robotic Cells , 2005, SIAM Rev..

[26]  MengChu Zhou,et al.  Analysis of Wafer Sojourn Time in Dual-Arm Cluster Tools With Residency Time Constraint and Activity Time Variation , 2010, IEEE Transactions on Semiconductor Manufacturing.

[27]  H. Neil Geismar,et al.  Approximation algorithms for k , 2005, Eur. J. Oper. Res..

[28]  T. C. Edwin Cheng,et al.  Complexity of cyclic scheduling problems: A state-of-the-art survey , 2010, Comput. Ind. Eng..

[29]  Chelliah Sriskandarajah,et al.  Scheduling robotic cells served by a dual-arm robot , 2012 .

[30]  H. Neil Geismar,et al.  Robotic Cells with Parallel Machines: Throughput Maximization in Constant Travel-Time Cells , 2004, J. Sched..

[31]  Hirotaka Nakayama,et al.  Theory of Multiobjective Optimization , 1985 .

[32]  Appa Iyer Sivakumar,et al.  Job shop scheduling techniques in semiconductor manufacturing , 2006 .

[33]  R. Musmanno,et al.  Label Correcting Methods to Solve Multicriteria Shortest Path Problems , 2001 .

[34]  Tung-Kuan Liu,et al.  Applications of Multi-objective Evolutionary Algorithms to Cluster Tool Scheduling , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[35]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[36]  A. G. Hamilton Logic for Mathematicians , 1978 .

[37]  H. G. Daellenbach,et al.  Note on Multiple Objective Dynamic Programming , 1980 .

[38]  M. Kostreva,et al.  Time Dependency in Multiple Objective Dynamic Programming , 1993 .

[39]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[40]  MengChu Zhou,et al.  Petri Net-Based Scheduling of Single-Arm Cluster Tools With Reentrant Atomic Layer Deposition Processes , 2011, IEEE Transactions on Automation Science and Engineering.