A neural network model for point defect microcavities of two-dimensional GaAs media background photonic crystals

Abstract

This paper aims to address the challenge of constructing, training, and optimizing a neural network model for the point defect microcavity of a two-dimensional GaAs media background photonic crystal. The objective is to achieve more accurate predictions and analyses of photon modes, thereby enhancing the performance of the microcavity. Due to the structural complexity and multi-parameter nature of point defect microcavities, traditional analysis methods often fail to capture their optical behavior adequately. Consequently, there is a need for a novel approach to establishing an optical model for point defect microcavities, enabling precise predictions of their optical properties and optimal design. In this study, the MPB computing tool developed by MIT, in conjunction with the programming language Scheme, MATLAB, and the Ubuntu virtual machine platform, was utilized for data acquisition. The neural network model was employed to process the input data of the photonic crystal point defect microcavity, leading to predictions and analyses. This interdisciplinary approach results in a high-precision model of the microcavity. The neural network model is capable of nonlinear mapping and prediction by learning the patterns and features of the data. Additionally, this combination holds the promise of enabling more efficient optical information processing and increased data transfer rates. The relationship between the energy band data and the structure of photonic crystals exhibits a high degree of nonlinearity. The neural network model proposed in this paper can infer the structure of photonic crystals based on the photon energy band (dispersion relation ω-K) of micro-cavity structures with point defects in different photonic crystals. This provides a valuable reference for more accurate predictions and analyses of photon modes in complex photonic crystal micro-cavities, ultimately leading to improved micro-cavity performance.