Design of a transmissive metasurface antenna using deep neural networks
Jaebum Noh, Yong-Hyun Nam, Sunae So, Chihun Lee, Sun‐Gyu Lee, Yongjune Kim, Tae-Hyung Kim, Jeong‐Hae Lee, Junsuk Rho
Abstract
This article presents design methods for a transmissive metasurface antenna composed of four layers of meta-structures based on the deep neural network (DNN). Owing to the structural complexity as well as side effects such as couplings among the adjacent meta-structures, the conventional design of metasurface unit cell strongly relies on the researcher’s intuition as well as time-consuming iterative simulations. A design method for a metasurface antenna unit cell with a size of a quarter wavelength operating at a frequency of 5.8GHz is presented. We describe two unique implementations for designing the target metasurfaces: 1) utilizing the inverse network 2) data augmentation by the forward network and a random search algorithm. With the usage of the two DNNs, the average transmittance of the unit cells is improved by about 0.024 than that of the unit cells designed by the conventional approach. This research invokes the application of DNN in designing antennas and other structures operating at radio frequency.