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Masking and Demasking Agents01:19

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    This study introduces a new source mask optimization method for extreme ultraviolet (EUV) lithography, significantly reducing pattern errors in integrated circuit manufacturing by up to 94.7%. The technique enhances imaging quality for advanced technology nodes.

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    Area of Science:

    • Integrated circuit manufacturing
    • Advanced lithography techniques
    • Computational electromagnetics

    Background:

    • Extreme ultraviolet (EUV) lithography is crucial for fabricating advanced integrated circuits.
    • Source Mask Optimization (SMO) is a key resolution enhancement technique (RET) in EUV lithography.
    • Improving imaging quality in EUV lithography is essential for manufacturing smaller and more powerful chips.

    Purpose of the Study:

    • To propose an advanced Source Mask Optimization (SMO) method for EUV lithography.
    • To enhance imaging quality and reduce pattern errors in integrated circuit manufacturing.
    • To improve the efficiency and manufacturability of the optimized mask.

    Main Methods:

    • Developed an SMO method utilizing a thick mask model and the social learning particle swarm optimization (SL-PSO) algorithm.
    • Pre-calculated and stored thick mask model parameters for efficient computation.
    • Tuned initialization parameters for the mask optimization (MO) stage to boost efficiency and manufacturability.
    • Validated results using rigorous electromagnetic simulations on three target patterns.

    Main Results:

    • Achieved significant reductions in pattern errors (PE) between the printed image and target patterns.
    • Reduced PE by 94.7% for the first target pattern.
    • Reduced PE by 76.9% and 80.6% for the second and third target patterns, respectively.
    • Demonstrated improved imaging quality and manufacturability of the optimized mask.

    Conclusions:

    • The proposed SMO method effectively improves imaging quality in EUV lithography.
    • The SL-PSO algorithm combined with a thick mask model offers a robust approach for SMO.
    • The method shows significant potential for enhancing the manufacturing of advanced integrated circuits.