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Wavefront correction with a diffractive optical neural network in-the-loop.

Fansanqiu Li, Xianjin Liu, Jun-Jun Xiao

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    Summary
    This summary is machine-generated.

    This study introduces an all-optical wavefront calibration using a diffractive optical neural network (DONN). This method achieves real-time correction without sensors, offering broad generalization for optical systems.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Optical Engineering

    Background:

    • Wavefront correction is critical across diverse scientific and technological fields.
    • Existing wavefront correction methods often require wavefront sensors and can be complex.
    • Challenges remain in achieving real-time, sensorless, and broadly applicable wavefront correction.

    Purpose of the Study:

    • To develop an all-optical wavefront calibration routine.
    • To enable real-time wavefront correction without the need for a wavefront sensor.
    • To demonstrate a generalized and adaptable wavefront correction approach.

    Main Methods:

    • Proposed an all-optical wavefront calibration routine utilizing a diffractive optical neural network (DONN).
    • The DONN comprises pre-trained multi-layer phase modulation pixel arrays.
    • Implemented a DONN-in-the-loop approach for optical system calibration.

    Main Results:

    • Achieved real-time wavefront correction without relying on a wavefront sensor.
    • The optimized DONN demonstrated the capability to compensate for unknown wavefront distortions.
    • The system exhibited broad generalization, requiring no task-specific retraining for new distortions.

    Conclusions:

    • The proposed method offers a rapid and efficient approach to optical system calibration.
    • Established a broadly applicable and nearly all-optical wavefront correction technique.
    • The DONN-in-the-loop system presents a significant advancement in optical wavefront control.