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Fast heuristic-based source mask optimization for EUV lithography using dual edge evolution and partial sampling.

Zinan Zhang, Sikun Li, Xiangzhao Wang

    Optics Express
    |July 16, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a faster Source Mask Optimization (SMO) method for Extreme Ultraviolet (EUV) lithography using dual edge evolution and partial sampling. The technique enhances optimization efficiency and speed for advanced technology nodes.

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    Area of Science:

    • Semiconductor manufacturing
    • Advanced lithography techniques
    • Optical engineering

    Background:

    • Extreme Ultraviolet (EUV) lithography is critical for fabricating advanced semiconductor technology nodes.
    • Source Mask Optimization (SMO) is a key Resolution Enhancement Technique (RET) in EUV lithography.
    • Heuristic-based SMO methods require efficiency and speed improvements.

    Purpose of the Study:

    • To propose a fast SMO method for EUV lithography.
    • To enhance the optimization efficiency and speed of heuristic algorithms.
    • To improve imaging quality across different focal planes.

    Main Methods:

    • A novel SMO method combining dual edge evolution and partial sampling strategies.
    • Source Optimization (SO) stage: sequential optimization of source point position and intensity.
    • Mask Optimization (MO) stage: sequential optimization of main features (MF) and sub-resolution assistant features (SRAF).
    • Dimensionality reduction via partial sampling encoding based on source sparsity.

    Main Results:

    • Significant improvement in optimization efficiency and speed.
    • Demonstrated superiority over previous methods, particularly for large, complex patterns.
    • Enhanced imaging quality at various focal planes through SRAF optimization.

    Conclusions:

    • The proposed dual edge evolution and partial sampling SMO method offers a substantial advancement in EUV lithography.
    • The dimensionality reduction strategies effectively accelerate the optimization process.
    • This method is highly effective for optimizing complex patterns in advanced semiconductor fabrication.