Predicting cluster tool behavior with slow down factors

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Predicting cluster tool behavior with slow down factors

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Winter Simulation Conference archive
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come table of contents

Washington D.C.

SESSION: Semiconductor manufacturing: semiconductor manufacturing equipment modeling table of contents

Pages 1755-1760

Year of Publication: 2007

ISBN:1-4244-1306-0

Authors

Robert Unbehaun	Dresden University of Technology, Dresden, Germany
Oliver Rose	Dresden University of Technology, Dresden, Germany

Sponsors

INFORMS-SIM : Institute for Operations Research and the Management Sciences: Simulation Society
NIST : National Institute of Standards and Technology
(SCS) : The Society for Modeling and Simulation International
ACM/SIGSIM : Association for Computing Machinery: Special Interest Group on Simulation
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/SMC : Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society

Publisher

IEEE Press Piscataway, NJ, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 15, Citation Count: 1

Additional Information:

abstract references cited by collaborative colleagues

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ABSTRACT

Cluster tools are representatives of a special kind of tool where process times of jobs depend on the combination in which they are processed together on the tool and hence, depending on the sequence in which they are processed at a tool. To evaluate schedules of jobs to be processed at such a tool an estimation method is needed since a detailed simulation takes too long. In this paper, we present a method based on slow down factors which produces promising results and gives hints for the development of intelligent scheduling methods for this kind of tools.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Atherton, L. F., and R. W. Atherton. 1995. Wafer Fabrication: Factory Performance and Analysis. Kluwer.
	2	Joo, Y.-J., and T.-E. Lee. 2004. Virtual Control: A Virtual Cluster Tool for Testing and Verifying a Cluster Tool Controller and a Scheduler. IEEE Robotics & Automation Magazine, 11(3), 33--49.
	3	Todd LeBaron , Joerg Domasche, Optimizing robot algorithms with simulation, Proceedings of the 37th conference on Winter simulation, December 04-07, 2005, Orlando, Florida
	4	Niedermayer, H., and Rose, O. 2003. A Simulation-based Analysis of the Cycle Time of Cluster Tools in Semiconductor Manufacturing. In Proceedings of the 15th European Simulation Symposium.
	5	Perkinson, T. L., P. K. McLarty, R. S. Gyurcsik, and R. K. Cavin III. 1994. Single-Wafer Cluster Tool Performance: An Analysis of Throughput. IEEE Transactions on Semiconductor Manufacturing, 7 (3), 369--373.
	6	Perkinson, T. L., P. K. McLarty, R. S. Gyurcsik, and R. K. Cavin III. 1996. Single-Wafer Cluster Tool Performance: An Analysis of the Effects of Redundant Chambers and Revisitation Sequences on Throughput. IEEE Transactions on Semiconductor Manufacturing, 9 (3), 384--400.
	7	Robert Unbehaun , Oliver Rose, The use of slow down factors for the analysis and development of scheduling algorithms for parallel cluster tools, Proceedings of the 38th conference on Winter simulation, December 03-06, 2006, Monterey, California

CITED BY

Julie Christopher, Study of optimal load lock dedication for cluster tools, Proceedings of the 40th Conference on Winter Simulation, December 07-10, 2008, Miami, Florida

Collaborative Colleagues:

Robert Unbehaun: colleagues

Oliver Rose: colleagues

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