LAUSR

A prior-knowledge-guided deep-learning-enabled (PK-DL) synthesis method is proposed to design the metacells with the full-range amplitude and phase control for suppressing the sidelobe levels (SLLs) of a metalens antenna. The PK-DL synthesis method is based on a conditional deep convolutional generative adversarial network (cDCGAN). First, the metacells are pixelated to offer the higher degrees of design freedom and expand their transmission responses to full 2-D coverage of amplitude and phase responses so that a full-range metacell dataset is built up to simultaneously control the amplitude and phase of a single metacell. Conventionally, by optimizing the dimensions of predefined patterns of a metacell, only the phase response of a single metacell is achieved with a specific amplitude response, and vice versa. The metalens design based on the synthesized metacells has much higher degrees of freedom so that the metalens antenna has the potential to break the ceiling of performance and to realize more functions than existing metalens designs. As examples, two types of single-lens metalens antennas are designed for sidelobe suppression using the proposed metacell dataset. The first metalens is excited by a dipole antenna with an omnidirectional radiation pattern to demonstrate the simultaneous control of amplitude and phase responses using the synthesized metacells. The second metalens fed by a standard horn antenna is designed to suppress the SLLs in both the E- and H-planes. The horn-fed square metalens has an aperture of $13.2\times 13.2\,\,\lambda _{0}$ at 12 GHz. Measurement shows that the proposed metalens antenna realizes the gain of 26.9 dBi at 12 GHz and the first SLLs in the E- and H-planes are −33.2 and −30.9 dB, respectively. The proposed synthesis method greatly expands the boundary of amplitude and phase responses of a single metacell so that the metacell dataset offers much higher degrees of freedom for not only metalenses but also other metasurfaces.