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A parameterized mask model for lithography simulation

Published: 26 July 2009 Publication History

Abstract

We formulate the mask modeling as a parametric model order reduction problem based on the finite element discretization of the Helmholtz equation. By using a new parametric mesh and a machine learning technique called Kernel Method, we convert the nonlinearly parameterized FEM matrices into affine forms. This allows the application of a well-understood parametric reduction technique to generate compact mask model. Since this model is based on the first principle, it naturally includes diffraction and couplings, important effects that are poorly handled by the existing heuristic mask models. Further more, the new mask model offers the capability to make a smooth trade-off between accuracy and speed.

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A. K. T. Wong, Resolution Enhancement Techniques in Optical Lithography, SPIE, Bellingham, WA, 2001.
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S. Burger, R. Kohle, L. Zschiedrich, H. Nguyen, F. Schmidt, R. Marz, and C. Nolscher, "Rigorous simulation of 3D masks," in Proc. SPIE, 2006, vol. 6349.
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Jaione Tirapu-Azpiroz; Paul Burchard; Eli Yablonovitch, "Boundary layer model to account for thick mask effects in photolithography," in Advanced Lithography, Proc. SPIE, Feb. 2003, p. 1611.
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Cited By

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  • (2009)Incremental large-scale electrostatic analysisIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2009.203026728:11(1641-1653)Online publication date: 1-Nov-2009

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    cover image ACM Conferences
    DAC '09: Proceedings of the 46th Annual Design Automation Conference
    July 2009
    994 pages
    ISBN:9781605584973
    DOI:10.1145/1629911
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    Published: 26 July 2009

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    Author Tags

    1. lithography
    2. mask model
    3. parameterized model order reduction

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    • (2009)Incremental large-scale electrostatic analysisIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2009.203026728:11(1641-1653)Online publication date: 1-Nov-2009

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