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反向设计的集成全光学非线性激活器用于光学计算.

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    研究人员开发了用于光学神经网络 (ONN) 的新型光学非线性激活器. 这些激活器可以提高机器学习任务的准确性,克服当前电子系统的局限性.

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    科学领域:

    • 光子学和光学工程的工程.
    • 人工智能和机器学习
    • 材料科学 材料科学 材料科学

    背景情况:

    • 电子神经网络面临着算术和能量限制.
    • 光学神经网络 (ONN) 提供了一个潜在的解决方案,但缺乏广泛实施所必需的光学非线性.
    • 现有的ONN设计难以实现高效和紧的非线性激活.

    研究的目的:

    • 为ONN设计和实施超紧的全光学非线性激活器.
    • 解决阻碍ONN采用的非线性瓶问题.
    • 提高ONN在机器学习任务中的性能和稳定性.

    主要方法:

    • 反向设计将附加方法与Kerr非线性结合起来.
    • 利用Kerr和热光学 (TO) 效应来产生非线性响应.
    • 展示传输作为计算和结构作为功能原则.

    主要成果:

    • 成功逆向设计了三个超紧的全光学非线性激活器.
    • 达到2.34mW的最低激活值.
    • 通过结合非线性激活,显著提高了MNIST任务准确度,从88.15%提高到93.25%.
    • 在ONN拓中证明了对相位错误的稳定性.

    结论:

    • 开发的非线性激活器有效地提高了ONN的性能和表达力.
    • 反向设计方法为创建高效的光学计算组件提供了可行的途径.
    • 这项工作为可扩展的芯片级光学神经网络铺平了道路.