Photonic waveguides are prime candidates for integrated and parallel photonic interconnects. Such interconnects correspond to large-scale vector matrix products, which are at the heart of neural network computation. However, parallel interconnect circuits realized in two dimensions, for example by lithography, are strongly limited in size due to disadvantageous scaling. We use three dimensional (3D) printed photonic waveguides to overcome this limitation. 3D optical-couplers with fractal topology efficiently connect large numbers of input and output channels, and we show that the substrate's footprint area scales linearly. Going beyond simple couplers, we introduce functional circuits for discrete spatial filters identical to those used in deep convolutional neural networks.

中文翻译：

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用于光子神经网络可扩展集成的三维波导互连

光子波导是集成和并行光子互连的主要候选者。这种互连对应于大规模向量矩阵乘积，这是神经网络计算的核心。然而，例如通过光刻在二维中实现的并行互连电路由于不利的缩放而在尺寸上受到强烈限制。我们使用三维 (3D) 印刷光子波导来克服这一限制。具有分形拓扑结构的 3D 光耦合器有效地连接了大量的输入和输出通道，我们展示了基板的足迹面积线性缩放。除了简单的耦合器之外，我们还介绍了与深度卷积神经网络中使用的那些相同的离散空间滤波器的功能电路。