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High-precision, real-time wavefront sensing via sparse diffractive deep neural networks.

Jiaxin Long, Yibin Xiong, Zeyu Zhou

    Optics Express
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    Summary

    A new sparse deep neural network (SD2NN) enhances wavefront sensing accuracy by 45.4% while reducing size by 82%. This breakthrough enables compact, high-precision optical system components.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Optical Engineering

    Background:

    • Wavefront sensing is crucial for adaptive optics in communication and imaging.
    • Current diffraction deep neural networks (D2NNs) face challenges in compactness and prediction accuracy.

    Purpose of the Study:

    • To develop a compact and accurate wavefront sensing technology.
    • To improve the real-time detection efficiency of optical systems.

    Main Methods:

    • Designed a multi-layer compact D2NN using Bayesian optimization, termed sparse D2NN (SD2NN).
    • Investigated the impact of network depth and neuron size on SD2NN performance.
    • Determined general laws for diffraction layer distance and neuron size.

    Main Results:

    • Achieved a 45.4% reduction in root-mean-square error (RMSE) for wavefront sensing.
    • Reduced the axial length by approximately 82% compared to unoptimized D2NNs.
    • Attained a minimum layer distance of 8.77 mm.

    Conclusions:

    • The proposed SD2NN offers high-precision, real-time direct wavefront sensing.
    • This method facilitates the design of miniaturized, integrated wavefront sensing chips.
    • The findings provide a reliable approach for advancing optical system components.