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    Area of Science:

    • Semiconductor manufacturing
    • Optical imaging techniques
    • Artificial intelligence in metrology

    Background:

    • Wide-area microdefect inspection is critical for semiconductor quality control.
    • Current methods, like deep learning ghost imaging (DLGI), offer sensitivity and speed but are limited in resolution by illumination patterns.
    • High-resolution imaging is essential for detecting increasingly smaller defects.

    Purpose of the Study:

    • To develop a novel imaging method that overcomes the resolution limitations of existing deep learning ghost imaging techniques.
    • To enhance the sensitivity, speed, and resolution of microdefect inspection in semiconductor manufacturing.
    • To enable accurate detection and localization of sub-pixel defects.

    Main Methods:

    • Proposed sub-pixel deep learning ghost imaging (SP-DLGI) by leveraging illumination patterns to enhance resolution.
    • Developed a deep learning model to analyze subtle intensity variations caused by illumination blurring.
    • Predicted sub-pixel defect positions based on the analyzed intensity variations.

    Main Results:

    • SP-DLGI achieved rapid, high-sensitivity, and high-resolution imaging capabilities.
    • Experimental validation demonstrated SP-DLGI's effectiveness in predicting defect positions at 8K resolution.
    • The method successfully identified sub-pixel defect locations with enhanced accuracy.

    Conclusions:

    • SP-DLGI offers a significant advancement in microdefect inspection for the semiconductor industry.
    • The proposed technique provides a viable solution for achieving high-resolution, sensitive, and rapid defect detection.
    • SP-DLGI enables precise localization of sub-pixel defects, improving overall semiconductor quality control.