Factory cycle-time prediction with a data-mining approach

Phillip Backus, Mani Janakiram, Shahin Mowzoon, George Runger, Amit Bhargava

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

An estimate of cycle time for a product in a factory is critical to semiconductor manufacturers (and in other industries) to assess customer due dates, schedule resources and actions for anticipated job completions, and to monitor the operation. Historical data can be used to learn a predictive model for cycle time based on measured and calculated process metrics (such as work-in-progress at specific operations, lot priority, product type, and so forth). Such a method is relatively easy to develop and maintain. Modern data mining algorithms are used to develop nonlinear predictors applicable to the majority of process lots, and three methods are compared here. They are compared with respect to performance in actual manufacturing data (to predict times for both final and intermediate steps) and for the feasibility to maintain and rebuild the model.

Original languageEnglish (US)
Pages (from-to)252-258
Number of pages7
JournalIEEE Transactions on Semiconductor Manufacturing
Volume19
Issue number2
DOIs
StatePublished - May 1 2006

Keywords

  • Due date
  • Scheduling
  • Statistical models
  • Work-in-progress (WIP)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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