Hybrid Method for Inverse Design of Orbital Angular Momentum Transmission Fiber Based on Neural Network and Optimization Algorithms
Haibo Qin, Wei Huang, Binbin Song, Shengyong Chen
Abstract
A hybrid method of combining Neural Networks (NNs) and optimization algorithms is proposed for the inverse design of orbital angular momentum (OAM) transmission fiber with high efficiency and precision. NNs are used to predict the optical properties of OAM transmission fibers with high calculation accuracy and speed, including chromatic dispersion and effective index difference ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Delta n_{eff}$</tex-math></inline-formula> ). Then the trained prediction models are combined with particle swarm optimization (PSO) and multi-objective particle swarm optimization (MOPSO) algorithms for the inverse design of OAM transmission fiber respectively. After analyzing the differences and properties of the hybrid PSO-NN and MOPSO-NN algorithms in detail, we designed an OAM transmission fiber with extreme low chromatic dispersion through the hybrid MOPSO-NN algorithm. The proposed method can avoid the single solution problem of tandem neural network and output a number of appropriate fiber structures according to the design requirements, which provides a new approach for the inverse design of optical structure with extreme performance. Finally, the accuracy and effectiveness of the proposed hybrid method are compared and demonstrated through COMSOL Multiphysics.