Inverse Design of Multi-band Reflective Polarizing Metasurfaces Using Generative Machine Learning
Abstract
Electromagnetic linear-to-circular polarization converters with wide- and multi-band capabilities can simplify antenna systems where circular polarization is required. Multi-band solutions are attractive in satellite communication systems, which commonly have the additional requirement that the sense of polarization is reversed {between adjacent bands}. However, the design of these structures using conventional \textit{ad hoc} methods relies heavily on empirical methods. Here, we employ a data-driven approach integrated with a generative adversarial network to explore the design space of the polarizer meta-atom thoroughly. Dual-band and triple-band reflective polarizers with stable performance over incident angles up to and including , {corresponding to typical reflector antenna system requirements}, are synthesized using the proposed method. The feasibility and performance of the designed polarizer is validated through measurements of a fabricated prototype.
Cite
@article{arxiv.2207.04019,
title = {Inverse Design of Multi-band Reflective Polarizing Metasurfaces Using Generative Machine Learning},
author = {Parinaz Naseri and George Goussetis and Nelson J. G. Fonseca and Sean V. Hum},
journal= {arXiv preprint arXiv:2207.04019},
year = {2022}
}