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使用深度空间衍射神经网络进行光学模式操纵.

Zhengsen Ruan, Bowen Wang, Jinlong Zhang

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    此摘要是机器生成的。

    空间衍射神经网络 (SDNN) 实现了先进的光学模式操纵. 本研究引入了一种新的模拟和优化方法,展示了高效的OAM模式解复和多功能模式转换.

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    科学领域:

    • 光子学和光学工程的工程.
    • 计算物理 计算物理
    • 人工智能的人工智能

    背景情况:

    • 空间衍射神经网络 (SDNN) 为光场操纵提供了独特的能力.
    • 高效控制和转换光学模式对于先进的光学系统至关重要.

    研究的目的:

    • 研究SDNNs用于光学模式操纵的理论模型和应用.
    • 为SDNNs开发和验证一个新的模拟和优化框架.
    • 为了证明SDNNs在解复和转换各种光学模式中的性能.

    主要方法:

    • 开发了一种使用矩阵乘法衍射传输模拟方法.
    • 采用神经网络梯度下降算法来优化SDNN的参数.
    • 设计并优化了SDNN结构,用于轨道角动量 (OAM) 模式解复和Hermite-Gaussian (HG) 转换为Laguerre-Gaussian (LG) /OAM模式.

    主要成果:

    • 通过分别使用4层,10层和50层SDNN实现了5,11层和100层OAM模式的解复.
    • 100 OAM 模式的解倍器显示了低平均损失 (0.52 dB) 和最小的交叉声 (-28.24 dB).
    • 通过10层SDNN证明了HG到LG和HG到OAM模式转换的成功.
    • 在玻璃基板上集成的SDNN用于实际的光子设备应用,展示了11-OAM模式的解复合器和25通道的OAM到HG模式转换器.

    结论:

    • SDNN为复杂的光学模式操纵提供了强大而通用的平台.
    • 开发的模拟和优化技术使得高性能衍射光学元件的设计成为可能.
    • SDNN显示出在光通信和信号处理领域的应用的巨大潜力.