TE–TM Balanced Wide-Angle Metacells for Low Scan-Loss Metalens Antenna Using Prior Knowledge-Guided Generative Deep Learning-Enabled Method

Abstract

The miniaturized cage-like metacell is proposed for TE–TM balanced wide-angle transmission using the prior knowledge (PK)-guided generative deep learning (DL) method, enabling a low scan-loss metalens antenna. An initial metacell topology and pattern generation rules are proposed, guided by physical constraints and engineering experience, and efficiently construct a high-degree-of-freedom (DoF) dataset for training a conditional deep convolutional generative adversarial network (cDCGAN). With a trained generator, diverse DL-enabled miniaturized cage-like metacells achieve a transmittance higher than 0.75 with fluctuations below 0.15 and a phase shift range of 295° with variations less than 15° at 10 GHz under TM and TE polarized incident waves from 0° to 45°. To verify the generative designs, a metalens antenna prototype consisting of the proposed metacells shows a realized gain of 26.2 dBi with an aperture efficiency of 36.3% and measured scan losses lower than 2.6 and 2.4 dB as TE- and TM-polarized beams scanning from −40° to 40° at 10 GHz.