Multifunctional Metasurface Inverse Design Based on Ultra-Wideband Spectrum Prediction Neural Network

Multifunctional metasurfaces have demonstrated extensive potential in various fields. During the design of metasurfaces, optimization of components for polarization, amplitude distribution, phase distribution, and other factors is necessary. This process typically demands expert involvement and is time-consuming. In this paper, a metasurface inverse design method is introduced that combines a high-precision ultra-wideband spectrum forward prediction utilizing a neural network and a genetic algorithm. A neural network is constructed and trained to accurately predict the amplitude and phase of a 16 x 16 discrete grid structure with high degrees of freedom in the frequency range of 0.5-2 THz. Leveraging the neural network's ultra-fast spectrum prediction capabilities (producing approximately 1000 spectra per second), the average optimization time for a single component is reduced to 1.5 min. Finally, the effectiveness of this inverse design method is validated through the design and simulation of multifunctional reflective deflection metasurfaces with two sets of 3-bit frequency multiplexing and polarization multiplexing. The proposed metasurface inverse design method offers a new approach for the rapid design of components in complex application scenarios and holds significant reference value for metasurface designers. This paper introduces a multifunctional metasurface inverse design method, integrating a neural network and genetic algorithm for fast, high-precision, and low manual-dependency design of components in complex application scenarios. Neural networks play a role in accurately predicting spectra, while genetic algorithm are responsible for finding the structure that best meets the design requirements within discrete grid structures.image