An online collaborative semiconductor yield forecasting system

Tin-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Yield forecasting is a very important task to a semiconductor manufacturing factory which is a typical group-decision-making environment. Namely, many experts will gather to predict the yields of products collaboratively. To enhance both the precision and accuracy of collaborative semiconductor yield forecasting, an online expert system is constructed in this study. The collaborative semiconductor yield forecasting system adopts the client-server architecture, and therefore the necessity for all experts to gather at the same place is relaxed, which is especially meaningful for a multiple-factory case. To demonstrate the applicability of the collaborative semiconductor yield forecasting system, an experimental system has been constructed and applied to two random-access-memory products in a real semiconductor manufacturing factory. Both the precision and accuracy of forecasting the yields of the two products were significantly improved. Besides, the collaborative semiconductor yield forecasting system was also considered as a convenient platform for the product engineers or quality control staff from different factories to share their opinions about the yield improvement process of a product being manufacturing with the same technology in multiple factories.

Original languageEnglish
Pages (from-to)5830-5843
Number of pages14
JournalExpert Systems with Applications
Volume36
Issue number3 PART 2
DOIs
StatePublished - 1 Jan 2009

Keywords

  • Collaborative
  • Expert system
  • Fuzzy neural
  • Semiconductor
  • Yield forecasting

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