Three-dimensional waveguide interconnects for scalable integration of photonic neural networks
Johnny Moughames, Xavier Porté, Michael Thiel, Gwenn Ulliac, Laurent Larger, Maxime Jacquot, Muamer Kadic, Daniel Brunner
Abstract
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 area and height scale linearly. Going beyond simple couplers, we introduce functional circuits for discrete spatial filters identical to those used in deep convolutional neural networks.