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    This study introduces a machine learning framework for accurately characterizing multilayer defects on extreme ultraviolet (EUV) lithography masks. The novel approach aids in precise defect compensation and repair for advanced semiconductor manufacturing.

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    Area of Science:

    • Semiconductor Manufacturing
    • Materials Science
    • Machine Learning Applications

    Background:

    • Mask defects in extreme ultraviolet (EUV) lithography, particularly multilayer defects, can significantly distort reflectivity and phase shifts.
    • Accurate geometric parameter characterization of these defects is crucial for effective compensation and repair strategies in advanced semiconductor fabrication.

    Purpose of the Study:

    • To develop and present a machine learning framework for predicting the geometric parameters of multilayer defects on EUV mask blanks.
    • To enable high-accuracy defect characterization for improved EUV lithography processes.

    Main Methods:

    • Implementation of a machine learning framework incorporating inception modules.
    • Utilization of cycle-consistent learning techniques for defect characterization.
    • Development of a novel approach for geometric parameter prediction of EUV mask defects.

    Main Results:

    • The proposed framework demonstrates high accuracy in predicting geometric parameters of multilayer defects.
    • The method provides a novel and effective way for defect characterization on EUV mask blanks.
    • Successful application of machine learning to address critical challenges in EUV lithography.

    Conclusions:

    • The developed machine learning framework offers a powerful tool for precise geometric parameter characterization of EUV mask multilayer defects.
    • This advancement is vital for enhancing defect compensation and repair, leading to improved yields in advanced node manufacturing.
    • The study highlights the potential of AI-driven solutions in optimizing semiconductor lithography processes.