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Related Experiment Videos

Mask design for optical microlithography--an inverse imaging problem.

Amyn Poonawala1, Peyman Milanfar

  • 1Computer Engineering Department, University of California, Santa Cruz, CA 95064, USA. amyn@soe.ucsc.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 16, 2007
PubMed
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This study introduces a novel algorithm for optical proximity correction mask design in optical microlithography. The method uses pixel-based mask representation and regularization to compensate for imaging system distortions, improving pattern transfer accuracy.

Area of Science:

  • Optics and Photonics
  • Semiconductor Manufacturing
  • Computational Imaging

Background:

  • Imaging systems inherently introduce distortions, such as blur, affecting signal fidelity.
  • Optical microlithography, crucial for semiconductor manufacturing, transfers circuit patterns but is susceptible to image degradation.
  • Prewarping techniques can mitigate these distortions by modifying the input signal.

Purpose of the Study:

  • To develop an algorithm for optical proximity correction (OPC) mask design in optical microlithography.
  • To address the inverse problem of compensating for imaging system losses in pattern transfer.
  • To create a framework applicable to various imaging coherence conditions.

Main Methods:

  • The study proposes a pixel-based mask representation for OPC.

Related Experiment Videos

  • A continuous function formulation is employed to define the mask.
  • Regularization techniques are utilized to control mask tone and complexity.

    • Main Results:

    • An algorithm was developed to solve an inverse problem approximating real-world optical lithography.
    • The method enables the synthesis of masks that compensate for imaging system distortions.
    • The framework's extension to coherent and partially coherent systems is discussed.

    Conclusions:

    • The developed algorithm offers a robust approach to OPC mask design.
    • The pixel-based, continuous function formulation with regularization provides effective distortion compensation.
    • The framework shows potential for broader application in advanced lithography processes.