Overcome chromatism of metasurface via Greedy Algorithm empowered by self-organizing map neural network.

Abstract

Chromatism generally exists in most metasurfaces. Because of this, the deflected angle of metasurface reflectors usually varies with frequency. This inevitably hinders wide applications of metasurfaces to broadband signal scenarios. Therefore, it is of great significance to overcome chromatism of metasurfaces. With this aim, we firstly analyze necessary conditions for achromatic metasurface deflectors (AMD) and deduce the ideal dispersions of meta-atoms. Then, we establish a Self-Organizing Map (SOM) Neural Network as a prepositive model to obtain a diversified searching map, which is then applied to Greedy Algorithm to search meta-atoms with the required dispersions. Using these meta-atoms, an AMD was designed and simulated, with a thickness about 1/15 the central wavelength. A prototype was fabricated and measured. Both the simulation and measurement show that the proposed AMD can achieve an almost constant deflected angle of 22° under normal incidence within 9.5-10.5GHz. This method may find wide applications in designing functional metasurfaces for satellite communications, mobile wireless communications and others.