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Data analytics and stochastic modeling in a semiconductor fab

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Listed:
  • Sugato Bagchi
  • Robert J. Baseman
  • Andrew Davenport
  • Ramesh Natarajan
  • Noam Slonim
  • Sholom Weiss

Abstract

The scale, scope and complexity of the manufacturing operations in a semiconductor fab lead to some unique challenges in ensuring product quality and production efficiency. We describe the use of various analytical techniques, based on data mining, process trace data analysis, stochastic simulation and production optimization, to address these manufacturing issues, motivated by the following two objectives. The first objective is to identify the sub‐optimal process conditions or tool settings that potentially affect the process performance and product quality. The second objective is to improve the overall production efficiency through better planning and resource scheduling, in an environment where the product mix and process flow requirements are complex and constantly changing. Copyright © 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • Sugato Bagchi & Robert J. Baseman & Andrew Davenport & Ramesh Natarajan & Noam Slonim & Sholom Weiss, 2010. "Data analytics and stochastic modeling in a semiconductor fab," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(1), pages 1-27, January.
    • Handle: RePEc:wly:apsmbi:v:26:y:2010:i:1:p:1-27
    DOI: 10.1002/asmb.828
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    References listed on IDEAS

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    1. Francis Sourd, 2009. "New Exact Algorithms for One-Machine Earliness-Tardiness Scheduling," INFORMS Journal on Computing, INFORMS, vol. 21(1), pages 167-175, February.
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