Prior-Knowledge-Guided Deep-Learning-Enabled Synthesis for Broadband and Large Phase Shift Range Metacells in Metalens Antenna

Abstract

A prior-knowledge-guided deep-learning-enabled (PK-DL) synthesis method is proposed for enhancing the transmission bandwidth and phase shift range of metacells used for the design of metalens antennas. The algorithm of conditional deep convolutional generative adversarial network (cDCGAN) is utilized in the proposed deep-learning (DL) method. Prior knowledge, including well-known fundamental electromagnetic theorems and experience in antenna design, is purposely applied at the early stage of the proposed method to strategically guide and speed up the synthesis. The proposed intelligent method provides the design of pixelated metacells with high degrees of freedom so that the key performance of the synthesized metacells exceeds the existing limit of conventional design methods by generating a rich profusion of cell patterns. For example, the synthesized triple-layer metacell achieves the −1 dB phase shift range of 330° breaking the limit of 308° derived by existing techniques. The proposed synthesis method also provides the additional capability to flexibly control the phase shift not only at the center frequency but also over a frequency range of interest. A Ku-band metalens antenna formed with the synthesized metacells demonstrates the achieved 1 and 3 dB gain bandwidths increase by 52.2% and 42.6%, respectively, compared to the metalens antenna using the well-known Jerusalem cross (JC) metacells. The proposed method extends the capability for the synthesis of metacells and metalens antennas with enhanced performance.