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    A temporal convolutional neural network models excimer laser voltage-energy relationships. This approach enables precise control for constant energy output in deep ultraviolet lithography applications.

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    Area of Science:

    • * Laser physics and optics
    • * Artificial intelligence and machine learning
    • * Semiconductor manufacturing technologies

    Background:

    • * Excimer lasers are critical for deep ultraviolet (DUV) lithography.
    • * Precise control of laser energy output is essential for process stability.
    • * Existing methods may lack the accuracy needed for advanced lithography.

    Purpose of the Study:

    • * To develop a predictive model for excimer laser voltage-energy characteristics.
    • * To enable stable, constant energy output for DUV lithography.
    • * To establish a continuous voltage-energy relationship for excimer lasers.

    Main Methods:

    • * A temporal convolutional neural network (TCN) was designed for voltage-energy modeling.
    • * The TCN utilizes current energy data to predict future energy output.
    • * Simulated initial energy data was used for unmeasured voltage-energy points.

    Main Results:

    • * A continuous excimer laser voltage-energy model was successfully established.
    • * The model accurately predicts energy output based on voltage input.
    • * The mean difference between measured and generated energy data was less than 0.5 mJ.

    Conclusions:

    • * The developed TCN model provides an effective method for controlling excimer laser energy.
    • * This facilitates stable operation in constant energy mode for DUV lithography.
    • * The model enhances the precision and reliability of excimer laser applications.