Deep Learning-Enabled Generative Antenna Design: Review and Prospects

This paper explores generative antenna design (GAD) and optimization using neural network (NN)-based deep learning (DL) methods. We first outline traditional antenna design and the challenges of applying NN-DL synthesis, particularly with generative adversarial networks (GANs). While conventional approaches reach performance limits, applying NN-DL generative methods to designs with increased degrees of freedom offers new possibilities for enhanced performance, additional functionality, and knowledge discovery. We then review the historical development and diverse applications of NN-based antenna optimization, covering individual elements, multi-antennas, and periodic structures. Key algorithmic concepts such as conditional GANs and variational autoencoders (VAEs) are discussed, emphasizing the role of prior knowledge in GAD. Recent advancements demonstrate how DL-enabled synthesis can surpass phase shift limits, control dispersion, and enhance metalens antenna gain bandwidth by 52%. The approach also enables full-range amplitude and phase control, effectively reducing sidelobe levels to below −30 dB. Finally, we highlight the significance of prior knowledge in GAD, distinguishing GAD from conventional parametric optimization. We discuss the challenges, future trends, and broader impacts of NN-driven generative design in next-generation antenna and electromagnetic device engineering.