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Optical mode manipulation using deep spatial diffractive neural networks.

Zhengsen Ruan, Bowen Wang, Jinlong Zhang

    Optics Express
    |June 11, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Spatial diffractive neural networks (SDNNs) enable advanced optical mode manipulation. This study introduces a novel simulation and optimization method, demonstrating high-efficiency OAM mode demultiplexing and versatile mode conversion.

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    Area of Science:

    • Photonics and Optical Engineering
    • Computational Physics
    • Artificial Intelligence

    Background:

    • Spatial diffractive neural networks (SDNNs) offer unique capabilities for light field manipulation.
    • Efficiently controlling and converting optical modes is crucial for advanced optical systems.

    Purpose of the Study:

    • To investigate theoretical models and applications of SDNNs for optical mode manipulation.
    • To develop and validate a novel simulation and optimization framework for SDNNs.
    • To demonstrate the performance of SDNNs in demultiplexing and converting various optical modes.

    Main Methods:

    • Developed a diffractive transmission simulation method using matrix multiplication.
    • Employed a neural network gradient descent algorithm for parameter optimization of SDNNs.
    • Designed and optimized SDNN structures for orbital angular momentum (OAM) mode demultiplexing and Hermite-Gaussian (HG) to Laguerre-Gaussian (LG) / OAM mode conversion.

    Main Results:

    • Achieved demultiplexing of 5, 11, and 100 OAM modes using 4, 10, and 50-layer SDNNs, respectively.
    • The 100 OAM mode demultiplexer exhibited low average loss (0.52 dB) and minimal crosstalk (-28.24 dB).
    • Demonstrated successful HG to LG and HG to OAM mode conversions with a 10-layer SDNN.
    • Integrated SDNNs on a glass substrate for practical photonic device applications, showcasing an 11-OAM mode demultiplexer and a 25-channel OAM to HG mode converter.

    Conclusions:

    • SDNNs provide a powerful and versatile platform for complex optical mode manipulation.
    • The developed simulation and optimization techniques enable the design of high-performance diffractive optical elements.
    • SDNNs show significant potential for applications in optical communications and signal processing.