• DocumentCode
    2471351
  • Title

    Process variation aware OPC with variational lithography modeling

  • Author

    Yu, Peng ; Shi, Sean X. ; Pan, David Z.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    785
  • Lastpage
    790
  • Abstract
    Optical proximity correction (OPC) is one of the most widely used resolution enhancement techniques (RET) in nanometer designs to improve subwavelength printability. Conventional model-based OPC assumes nominal process parameters without considering process variations, due to prohibitive runtimes of lithography simulations across process windows. This is the first paper to propose a true process-variation aware OPC (PV-OPC) framework. It is enabled by the variational lithography modeling and guided by the variational edge placement error (V-EPE) metrics. Due to the analytical nature of our models, our PV-OPC is only about 2-3 times slower than the conventional OPC, but it explicitly considers the two main sources of process variations (dosage and focus) during OPC. Thus our post PV-OPC results are much more robust than the conventional OPC ones, in terms of both geometric printability and electrical characterization under process variations
  • Keywords
    nanolithography; proximity effect (lithography); semiconductor process modelling; electrical characterization; geometric printability; lithography simulations; nanometer designs; optical proximity correction; process-variation aware OPC framework; resolution enhancement techniques; subwavelength printability; variational edge placement error metrics; variational lithography modeling; Computer applications; Design automation; Focusing; Lithography; Manufacturing processes; Optical design; Optical design techniques; Robustness; Runtime; Timing; Algorithms; Design; Lithography modeling; OPC; Performance; Reliability; process variation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Design Automation Conference, 2006 43rd ACM/IEEE
  • Conference_Location
    San Francisco, CA
  • ISSN
    0738-100X
  • Print_ISBN
    1-59593-381-6
  • Type

    conf

  • DOI
    10.1109/DAC.2006.229324
  • Filename
    1688902