Building a photonic neural network based on multi-operand multimode interference ring resonators

Abstract

Photonic neural networks (PNNs) offer significant potential for enhancing deep learning networks, providing high-speed processing and low energy consumption. In this paper, we present a novel PNN architecture that employs nonlinear optical neurons using multi-operand 4×4 multimode interference (MMI) multi-operand ring resonators (MORRs) to efficiently perform vector dot-product calculations. This design is integrated into a photonic convolutional neural network (PCNN) with two convolutional layers and one fully connected layer. Simulation experiments, conducted using Lumerical and Ansys tools, demonstrated that the model achieved a high test accuracy of 98.26% on the MNIST dataset, with test losses stabilizing at approximately 0.04%. The proposed model was evaluated, demonstrating high computation speed, improved accuracy, low signal loss, and scalability. These findings highlight the model’s potential for advancing deep learning applications with more efficient hardware implementations.