Broadband achromatic metalens design based on predictive neural network and particle swarm optimization-genetic algorithm

Abstract

The main challenge in designing broadband achromatic metalenses is to achieve the desired phase distribution at different wavelengths and positions. Finding the exact relationship between phase modulation and the size or shape of individual nanopillars is a critical but time-consuming step. This paper presents a novel joint design framework predictive neural networks and particle swarm optimization-genetic algorithms, which combines predictive neural network (PNN) and particle swarm optimization-genetic algorithm (PSO-GA). The proposed framework aims to accurately predict the phase response of nanopillars using PNN, increase the number of phase data points to establish a one-to-one correspondence between the phase and nanopillar parameters in the design of broadband achromatic metalenses, and optimize the parameters of an individual nanostructure of the metalens using PSO-GA. To validate the efficacy of the proposed method, a broadband achromatic metalens for line polarization light in the range of 260–350 nm is designed. Numerical simulations demonstrate that the designed metalens exhibits achromatic focusing. The method proposed in this paper may find wider application in the design of more complex metasurface devices.