• Title of article

    Defect cluster recognition system for fabricated semiconductor wafers

  • Author/Authors

    Ooi، نويسنده , , Melanie Po-Leen and Sok، نويسنده , , Hong Kuan and Kuang، نويسنده , , Ye Chow and Demidenko، نويسنده , , Serge and Chan، نويسنده , , Chris، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2013
  • Pages
    15
  • From page
    1029
  • To page
    1043
  • Abstract
    The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry’s needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type).
  • Keywords
    feature extraction , Recognition , Semiconductor wafer fabrication , Defect cluster classification
  • Journal title
    Engineering Applications of Artificial Intelligence
  • Serial Year
    2013
  • Journal title
    Engineering Applications of Artificial Intelligence
  • Record number

    2125877