• DocumentCode
    169328
  • Title

    The importance of reporting both composite and maze yield for process split yield learning

  • Author

    Fan Zheng ; Piper, A. ; Karve, Gauri ; Kan Zhang ; Yongchun Xin ; Jang Sim ; Mazzotti, Jason J.

  • Author_Institution
    IBM, East Fishkill, NY, USA
  • fYear
    2014
  • fDate
    19-21 May 2014
  • Firstpage
    21
  • Lastpage
    25
  • Abstract
    Electrical composite yield for a given macro is calculated from many smaller macros that are called mazes. Maze yield can also be calculated. For defects that have a random distribution the composite and the maze yield always provide the same trend for process splits. Specifically the composite yield can be expressed by pk where p is the maze yield and k is the number of mazes. This is no longer true when systematic and random defects co-exist in the dataset. The reading of the composite yield alone can not provide sufficient information of the process impact. Here we provide five real-world case-studies where a complete picture of the electrical fail mechanism is obtained only by analyzing both the maze and the composite yield.
  • Keywords
    integrated circuit reliability; integrated circuit technology; integrated circuit yield; electrical composite yield; electrical fail mechanism; maze yield; process impact; process split yield learning; random defect; random distribution; smaller macro; systematic defect; Cloning; Correlation; Inspection; Market research; Monitoring; Optical imaging; Systematics; Composite Yield; Maze Yield; Process Characterization; Random Defect; Random Defect Model; Systematic Defect; Yield Enhancement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Semiconductor Manufacturing Conference (ASMC), 2014 25th Annual SEMI
  • Conference_Location
    Saratoga Springs, NY
  • Type

    conf

  • DOI
    10.1109/ASMC.2014.6846949
  • Filename
    6846949