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Gradient-based inverse extreme ultraviolet lithography.

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    This study introduces a new algorithm to enhance image fidelity in extreme ultraviolet (EUV) lithography by correcting optical proximity, flare, and resist effects. The method optimizes mask patterns for improved manufacturing at advanced technology nodes.

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    Area of Science:

    • Semiconductor Manufacturing
    • Optical Engineering
    • Computational Lithography

    Background:

    • Extreme ultraviolet (EUV) lithography is poised to succeed deep ultraviolet (DUV) lithography.
    • EUV systems present unique imaging challenges due to short wavelengths, reflective optics, and non-telecentric designs.
    • Accurate image synthesis is critical for advanced semiconductor fabrication.

    Purpose of the Study:

    • To develop a gradient-based inverse algorithm for EUV lithography.
    • To significantly improve image fidelity by compensating for multiple complex effects.
    • To optimize mask pattern design while ensuring manufacturability.

    Main Methods:

    • A gradient-based inverse algorithm is employed for image fidelity enhancement.
    • Optical proximity effect, flare, photoresist, and mask shadowing effects are comprehensively compensated.
    • A block-based iterative optimization refines mask features and subresolution assist features (SRAFs).
    • Mask shadowing is addressed using a calibrated shadowing model and retargeting.

    Main Results:

    • The algorithm effectively improves image fidelity in EUV lithography simulations.
    • Simulations demonstrate successful compensation of various optical and resist-related effects.
    • The method preserves mask manufacturability during pattern optimization.
    • Validation at 22 and 16 nm technology nodes confirms the algorithm's effectiveness.

    Conclusions:

    • The developed gradient-based inverse algorithm offers a robust solution for EUV lithography image enhancement.
    • Comprehensive compensation of multiple effects leads to superior image fidelity.
    • The approach supports the advancement of semiconductor manufacturing at critical technology nodes.