Abstract

Photonic deep neural networks can perform fast and energy-efficient classification through computation by propagation and low-loss optical interconnects. Here, a review of our work on the first integrated end-to-end photonic-electronic deep neural network for direct image classification that achieved a classification time of 570 ps is presented. Then, the first monolithically integrated trainable optical nonlinear activation function designed in a 45nm CMOS SOI process for scalable silicon photonic deep neural networks is introduced, which effectively mitigates process, voltage, and temperature variations, ensuring robustness in photonic deep networks.