Optical XOR logic gate design in two dimensional photonic crystal using ANN and PSO

In this paper, we present an all-optical XOR gate based on two-dimensional photonic crystals (PCs). The proposed gate is designed to be used in optical computing and all-optical logic applications, offering advantages such as fast computation and parallel processing. To enhance the design process and optimize its performance, we employ artificial neural network (ANN) to model the gate's behavior. A dataset of 555 samples, obtained from the Finite-Difference Time-Domain (FDTD) method, is used to train the ANN model. Subsequently, particle swarm optimization (PSO) is used to find optimal values of XOR design parameters, including the lattice constant and the radius of the rods. This optimization process leads to an optimized structure that enhances the gate's performance. The proposed all-optical XOR gate exhibits a range of output powers across the respective logical states (desired state): 0 (0), 0.995 (1), 0.935 (1), and 0.015 (0). The proposed structure achieved an acceptable contrast ratio of 17.947 dB. By leveraging the ANN-PSO approach to improve the performance of PCs, we demonstrate a promising strategy for developing complex and efficient optical computing systems.