Recognizing yield patterns through hybrid applications of machine learning techniques

Jang Hee Lee, Sung Ho Ha

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Yield management in semiconductor manufacturing companies requires accurate yield prediction and continual control. However, because many factors are complexly involved in the production of semiconductors, manufacturers or engineers have a hard time managing the yield precisely. Intelligent tools need to analyze the multiple process variables concerned and to predict the production yield effectively. This paper devises a hybrid method of incorporating machine learning techniques together to detect high and low yields in semiconductor manufacturing. The hybrid method has strong applicative advantages in manufacturing situations, where the control of a variety of process variables is interrelated. In real applications, the hybrid method provides a more accurate yield prediction than other methods that have been used. With this method, the company can achieve a higher yield rate by preventing low-yield lots in advance.

Original languageEnglish
Pages (from-to)844-850
Number of pages7
JournalInformation Sciences
Issue number6
StatePublished - 1 Mar 2009


  • Case-based reasoning
  • Feature weighting
  • Hybrid application
  • Machine learning
  • Semiconductor manufacturing
  • Yield management


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