Design of a Neural Network Model for Point-Defect Microcavities in Two-Dimensional Silicon-Based Dielectric Column Photonic Crystals

Abstract

Currently, circuits are becoming more and more highly integrated, but current electronic chip technology has difficulty in meeting the requirements for increased data transmission speed and capacity because of its characteristics of increased energy loss due to interactions between electronic components. In contrast, photonic technology is a promising solution due to its characteristics of high speed, wide bandwidth, and low interaction. Photonic crystals are materials with an artificial periodic dielectric structure, with photonic bandgap and localization properties that are critical to their performance. Photonic crystals, which use photons as an information transfer medium, allow flexible control of photon propagation, just as electrons are controlled in semiconductors, and the study of multifunctional photonic crystals is important for the construction of integrated optical circuits. This paper proposes a neural network-based model to analyze and predict the optical properties of point defect microcavities in 2D silicon-based dielectric column photonic crystals. By modeling the complex relationship between the structural parameters of the photonic crystal and its optical response, a theoretical approach for the design of 2D silicon dielectric column photonic crystals can be provided.