Using a Multiagent Scheduling System for Dedicated Machine Constraint in Semiconductor Manufacturing

We present a multiagent scheduling (MS) system to tackle the dedicated machine constraint in this paper. The dedicated machine constraint is one of the new issues of the photolithography machinery due to natural bias. Natural bias will impact the alignment of patterns between different photolithography layers. The dedicated machine constraint is the most important challenge to improve productivity and fulfill the request for customers in semiconductor manufacturing today. In this paper, the proposed MS system is based on a resource schedule and execution matrix (RSEM) and keeps the load balancing among photolithography machines during each scheduling step according to the current load among the photolithography machines in the production line. We describe the prototype system including the agents and the coordination strategies in the paper. We also demonstrate the simulation result that validated the proposed MS system.

[1]  A Koestler,et al.  Ghost in the Machine , 1970 .

[2]  P. R. Kumar Scheduling Manufacturing Systems of Re-Entrant Lines , 1994 .

[3]  Ren C. Luo,et al.  Multiagent based multisensor resource management system , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[4]  P. R. Kumar,et al.  Re-entrant lines , 1993, Queueing Syst. Theory Appl..

[5]  Vladimír Marík,et al.  Industrial adoption of agent-based technologies , 2005, IEEE Intelligent Systems.

[6]  Alan Liu,et al.  A Load Balancing Method for Dedicated Photolithography Machine Constraint , 2006, BASYS.

[7]  Karl G. Kempf,et al.  A hierarchical approach to production control of reentrant semiconductor manufacturing lines , 2003, IEEE Trans. Control. Syst. Technol..

[8]  Alan Liu,et al.  A Load Balancing Scheduling Approach for Dedicated Machine Constraint , 2006, ICEIS.

[9]  P. R. Kumar,et al.  Distributed scheduling based on due dates and buffer priorities , 1991 .

[10]  Michal Pechoucek,et al.  ExPlanTech: multiagent support for manufacturing decision making , 2005, IEEE Intelligent Systems.

[11]  Sunil Kumar,et al.  Queueing network models in the design and analysis of semiconductor wafer fabs , 2001, IEEE Trans. Robotics Autom..

[12]  Kenwood H. Hall,et al.  Methodologies and tools for intelligent agents in distributed control , 2005, IEEE Intelligent Systems.

[13]  Douglas H. Norrie,et al.  Holons and holarchies ~intelligent manufacturing systems\ , 1997 .

[14]  Amr Arisha,et al.  Intelligent simulation-based lot scheduling of photolithography toolsets in a wafer fabrication facility , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[15]  P. Leitao,et al.  ADACOR: a collaborative production automation and control architecture , 2005, IEEE Intelligent Systems.

[16]  Lars Mönch,et al.  Simulation-based solution of load-balancing problems in the photolithography area of a semiconductor wafer fabrication facility , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[17]  P.P. Chen,et al.  Load Balancing among Photolithography Machines in Semiconductor Manufacturing , 2006, 2006 3rd International IEEE Conference Intelligent Systems.

[18]  Ching-Chin Chern,et al.  Family-based scheduling rules of a sequence-dependent wafer fabrication system , 2003 .