Innovations in metamaterial and metasurface antenna design: The role of deep learning

Abstract

Metamaterials and metasurfaces have revolutionized antenna design by enabling unprecedented control over electromagnetic waves. This paper explores integrating deep learning (DL) techniques in designing and optimizing metamaterial and metasurface antennas, focusing on improvements in gain, bandwidth, and size reduction. The review considers modern methodologies, such as hybrid optimization techniques with DL combined with traditional methods such as genetic algorithms and evolutionary strategies. It also addresses the use of high-fidelity datasets generated from advanced simulations to train DL models for more efficient antenna design. The paper is structured into five main sections: an introduction to metamaterials and metasurfaces, a discussion on their electromagnetic behavior, a classification of different types, an overview of deep learning applications in antenna design, and a conclusion summarizing the current advances, challenges, and future directions. By emphasizing the potential of DL to streamline the design process and enhance antenna performance, this paper provides a valuable foundation for future research in electromagnetic metasurfaces.