• DocumentCode
    759792
  • Title

    Yield improvement using statistical analysis of process dates

  • Author

    Bergeret, François ; Le Gall, Caroline

  • Author_Institution
    Motorola Semiconducteurs S.A.S., Toulouse, France
  • Volume
    16
  • Issue
    3
  • fYear
    2003
  • Firstpage
    535
  • Lastpage
    542
  • Abstract
    Rapid yield improvement is necessary in modern wafer fabrication facilities to ensure profitability of huge investments. Statistical analysis of data is a valuable method for improving yield. Usual methods like statistical process control (SPC), designs of experiments (DOE) and mapping analysis are necessary, but not sufficient because of process complexity. This paper presents a new statistical approach for solving yield issues when the root cause comes from a failure at a single process stage. The advantage of this approach is the only data required are the process dates of the lots at each process stage and the probe results. Three different methods have been tested to solve these issues. Only one of them is detailed in this paper. The efficiency of this method is demonstrated on two real yield issues where the defective stage is known.
  • Keywords
    Bayes methods; Markov processes; Monte Carlo methods; data mining; integrated circuit yield; probability; statistical analysis; Bayesian method; IC manufacturing; Markov chain Monte Carlo algorithm; data mining; lot process dates; probe results; profitability; statistical approach; wafer fabrication facilities; yield improvement; Data mining; Fabrication; Integrated circuit yield; Investments; Probes; Process control; Process design; Semiconductor device manufacture; Statistical analysis; US Department of Energy;
  • fLanguage
    English
  • Journal_Title
    Semiconductor Manufacturing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0894-6507
  • Type

    jour

  • DOI
    10.1109/TSM.2003.815204
  • Filename
    1219501