Optical computing has a series of advantages over its electronic counterpart, e.g., low energy consumption, high speed, and intrinsic parallelism. Diffraction deep neural networks (D²NNs) are a prominent example capable of processing images directly without addressing the spatial locations of each element. Despite the great successes, the D²NNs typically utilize the multilayer framework and face the severe challenge of misalignment in the optical region. Herein, a single metasurface-based optical-electronic hybrid neural network (OENN) is proposed and experimentally demonstrated. The OENN is composed of a titanium dioxide (TiO₂) metasurface and a fully-connected electronic layer. The combination of nonlocal neural layer and nonlinear transformation has significantly expanded the neural network capacity. Consequently, the classification accuracy on handwritten digits recognition can still be as high as 98.05% without employing the architecture of cascaded metasurfaces. The OENN shall shed light on the practical applications of optical computing in the visible spectrum.

中文翻译：

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全介质超表面赋能光电子混合神经网络

与电子计算相比，光学计算具有一系列优势，例如低能耗、高速和内在并行性。衍射深度神经网络 (D ² NN ) 是一个突出的例子，它能够直接处理图像而无需处理每个元素的空间位置。尽管取得了巨大的成功，但 D ² NN 通常利用多层框架并面临光学区域未对准的严峻挑战。在此，提出并通过实验证明了一种基于超表面的光电混合神经网络（OENN）。OENN由二氧化钛（TiO ₂) 超表面和全连接电子层。非局部神经层与非线性变换相结合，显着扩展了神经网络容量。因此，在不采用级联超表面架构的情况下，手写数字识别的分类准确率仍可高达 98.05%。OENN 将阐明光学计算在可见光谱中的实际应用。