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Implementation of response surface methodology using variance reduction techniques in semiconductor manufacturing
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Source Winter Simulation Conference archive
Proceedings of the 33nd conference on Winter simulation table of contents
Arlington, Virginia
SESSION: Semiconductor manufacturing table of contents
Pages: 1225 - 1230  
Year of Publication: 2001
ISBN:0-7803-7309-X
Authors
Charles D. McAllister  The Pennsylvania State University, University Park, PA
Bertan Altuntas  The Pennsylvania State University, University Park, PA
Matthew Frank  The Pennsylvania State University, University Park, PA
Juergen Potoradi  Infineon Technologies, Advanced Logic SDN. BHD., Free trade zone Batu Berendam, 75914 Melaka, MALAYSIA
Sponsors
INFORMS/CS : Institute for Operations Research and the Management Sciences/College on Simulation
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
NIST : National Institute of Standards and Technology
ACM: Association for Computing Machinery
SCS : The Society for Computer Simulation International
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
ASA : American Statistical Association
Publisher
IEEE Computer Society  Washington, DC, USA
Bibliometrics
Downloads (6 Weeks): 2,   Downloads (12 Months): 15,   Citation Count: 0
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ABSTRACT

Semiconductor manufacturing is generally considered a cyclic industry. As such, individual producers able to react quickly and appropriately to market conditions will have a competitive advantage. Manufacturers who maintain low work in process inventory, ensure that specialized equipment is in good repair, and produce quality products at least possible cost will have the best opportunities to effectively compete and excel in these challenging venues. To support this nimble business model, our current efforts are directed toward creating efficient, accurate metamodels of the impact of maintenance policies on production efficiency. These validated polynomial approximations facilitate rapid exploration of the design region, compared with the original simulation models. The experiment design used for metamodel construction employed variance reduction techniques. When compared to a similar experiment design using independent streams, the variance reduction approach provided a decrease in standard error of the regression coefficients and smaller average error when validated against the simulation response.


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
Aguilar, R. A. 2000. Assembly and Test Process Overview. Internet document, ⟨http://www.eas.asu.edu/~masmlab/⟩, Arizona State University.
 
2
Barton, R. R. 2000. IE 578: Using Simulation Models for Engineering Design. Fall semester lecture notes, The Pennsylvania State University.
 
3
Factory Explorer User Manual. 1995. Pleasanton, CA: Wright Williams & Kelley.
 
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Potoradi, J., 2000. Documentation of Simulation Model for Semiconductor Backend Manufacturing, Technical Paper, Infineon Technologies, Inc.
 
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Schmeiser, B. W. 1999. IE 581: Simulation Design and Analysis. Spring semester lecture notes, Purdue University.
 
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Schruben, L. W. and B. H. Margolin. 1978. Pseudo-random number assignment in statistically designed simulation and distribution sampling experiments. Journal of the American Statistical Association 73, 504-525.

Collaborative Colleagues:
Charles D. McAllister: colleagues
Bertan Altuntas: colleagues
Matthew Frank: colleagues
Juergen Potoradi: colleagues