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Data analytics and stochastic modeling in a semiconductor fab
Author(s) -
Bagchi Sugato,
Baseman Robert J.,
Davenport Andrew,
Natarajan Ramesh,
Slonim Noam,
Weiss Sholom
Publication year - 2010
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.828
Subject(s) - computer science , analytics , scheduling (production processes) , semiconductor device fabrication , scope (computer science) , process (computing) , quality (philosophy) , product (mathematics) , production (economics) , industrial engineering , operations research , data science , engineering , operations management , philosophy , geometry , electrical engineering , mathematics , epistemology , wafer , economics , macroeconomics , programming language , operating system
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.