Neural network-based prediction and optimization of optical properties of two-dimensional GaAs dielectric background photonic crystal point-defect microcavities

Abstract

The aim of this study is to develop a neural network model of point-defect microcavities in two-dimensional GaAs dielectric background photonic crystals in order to accurately predict and optimize their optical properties. Point-defect microcavities are localized structures in photonic crystals with the ability to modulate optical modes and enhance light-matter interactions. However, existing methods are unable to fully capture their complexity and multiparametric properties. Therefore, present study propose to utilize a neural network model to quickly and accurately predict the optical properties of point-defect microcavities. With this model, present study can effectively address the challenges faced by conventional methods in design and optimization. The results of this study will promote the development of photonic devices and photonic integration technologies, and facilitate the application of photonics in the fields of information technology, communication, energy and biomedicine.