• DocumentCode
    1305122
  • Title

    Wafer Sort Bitmap Data Analysis Using the PCA-Based Approach for Yield Analysis and Optimization

  • Author

    Hsieh, Yeou-Lang ; Tzeng, Gwo-Hshiung ; Lin, T.R. ; Yu, Hsiao-Cheng

  • Author_Institution
    Customer Tech. Supporting Div., Taiwan Semicond. Manuf. Co., Hsinchu, Taiwan
  • Volume
    23
  • Issue
    4
  • fYear
    2010
  • Firstpage
    493
  • Lastpage
    502
  • Abstract
    Yield analysis is one of the most important subjects in IC companies. During the initial stage of new process development, several factors can greatly impact the yield simultaneously. Traditionally, several learning cycle iterations are required to solve yield loss issues. This paper describes a novel way to diagnose yield loss issues in less iteration. First, the failure classification of bitmap data is transferred to a new basis using principal component analysis. Second, the defective rates are calculated and the original bitmap data is reconstructed in the principal basis, allowing the yield loss space to be generated by Cluster Analysis. Third, physical failure analysis samples can be selected to solve yield loss issues. Furthermore, the new yield loss basis can be used to monitor the progress of yield improvement as a discriminate analysis measure for reducing failure patterns (bitmap failures).
  • Keywords
    electronic engineering computing; integrated circuit yield; principal component analysis; PCA; bitmap failure; cluster analysis; failure analysis; failure classification; failure patterns; learning cycle iteration; principal component analysis; wafer sort bitmap data analysis; yield analysis; yield optimization; Failure analysis; Integrated circuit manufacture; Principal component analysis; Process control; Yield estimation; Bitmap; cluster analysis; discriminate analysis; principal component analysis (PCA); yield analysis; yield loss space;
  • fLanguage
    English
  • Journal_Title
    Semiconductor Manufacturing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0894-6507
  • Type

    jour

  • DOI
    10.1109/TSM.2010.2065510
  • Filename
    5557827