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Flatness error control technology based on random forests.

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    Summary
    This summary is machine-generated.

    This study introduces a novel method using astigmatic measurement and machine learning to detect and compensate for motion stage flatness errors in laser direct-writing lithography, significantly improving writing accuracy.

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    Area of Science:

    • Precision Engineering
    • Optical Metrology
    • Machine Learning Applications

    Background:

    • Flatness errors in motion stages critically degrade writing accuracy in dual-beam super-resolution laser direct-writing lithography.
    • Existing methods struggle to precisely detect and compensate for these nanometer-scale Z-axis fluctuations during XY-plane scanning.

    Purpose of the Study:

    • To develop an integrated method for precise detection and feedforward compensation of motion stage flatness errors.
    • To enhance the writing accuracy and operational stability of laser direct-writing lithography systems.

    Main Methods:

    • Utilized a dual-cylindrical-lens astigmatic measurement technique with a four-quadrant detector (FQD) to convert Z-axis errors into focus error signals (FES).
    • Implemented random forest regression from ensemble learning to predict the spatial distribution of stage flatness errors based on historical data.
    • Employed a piezoelectric actuator for feedforward compensation driven by the predicted error map.

    Main Results:

    • Achieved nanometer-scale detection of Z-axis stage errors by analyzing spot profile variations.
    • Demonstrated significant reduction in flatness errors during planar motion using the random-forest-based feedforward strategy.
    • Markedly improved writing accuracy and operational stability of the lithography system.

    Conclusions:

    • The integrated dual-cylindrical-lens astigmatic measurement and random forest feedforward compensation method effectively mitigates motion stage flatness errors.
    • This approach offers a robust solution for enhancing precision in advanced lithography techniques.
    • The study highlights the potential of machine learning for real-time error correction in high-precision manufacturing.