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Automated semiconductor equipment modeling and model parameter estimation using MES data

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2 Author(s)
Kohn, R. ; Oliver Rose Inst. of Appl. Comput. Sci., Dresden Univ. of Technol., Dresden, Germany ; Werner, S.

Driven by upcoming simulation and optimization applications with increasing complexity, the demand for more detailed equipment models is growing. The effort to create and to parameterize practicable equipment models by providing capacity limitations and predicting processing times increases as well. Automated modeling strategies based on MES data using data mining techniques increase the efficiency of today's modeling policies remarkably.

Published in:

Advanced Semiconductor Manufacturing Conference (ASMC), 2010 IEEE/SEMI

Date of Conference:

11-13 July 2010

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