• DocumentCode
    1444135
  • Title

    Yield Learning Through Physically Aware Diagnosis of IC-Failure Populations

  • Author

    Blanton, Ronald DeShawn ; Tam, Wing Chiu ; Yu, Xiaochun ; Nelson, Jeffrey E. ; Poku, Osei

  • Author_Institution
    Carnegie Mellon Univ., Pittsburgh, PA, USA
  • Volume
    29
  • Issue
    1
  • fYear
    2012
  • Firstpage
    36
  • Lastpage
    47
  • Abstract
    A variety of yield-learning techniques are essential since no single approach can effectively find every manufacturing perturbation that can lead to yield loss. Test structures, for example, can range from being simple in nature (combs and serpentine structures for measuring defect-density and size distributions) to more complex, active structures that include transistors, ring oscillators, and SRAMs. Test structures are designed to provide seamless access to a given failure type: its size, its location, and possibly other pertinent characteristics.
  • Keywords
    SRAM chips; failure analysis; integrated circuit testing; integrated circuit yield; learning (artificial intelligence); oscillators; transistors; IC-failure populations; SRAM; manufacturing perturbation; physically aware diagnosis; ring oscillators; test structures; transistors; yield learning; yield loss; Accuracy; Arrays; Failure analysis; Integrated circuits; Learning systems; Manufacturing processes; System testing; DFM; Yield; diagnosis; failure analysis; layout; learning; quality; scan; test;
  • fLanguage
    English
  • Journal_Title
    Design & Test of Computers, IEEE
  • Publisher
    ieee
  • ISSN
    0740-7475
  • Type

    jour

  • DOI
    10.1109/MDT.2011.2178587
  • Filename
    6148305