Deep Learning for Inverse Design of Broadband Quasi-Yagi Antenna

Deep learning (DL) approaches have been increasingly adopted to design antenna autonomously. For obtaining geometry of the broadband quasi-Yagi antenna from its physic response images directly, we propose an inverse design approach based on the optimized bidirectional symmetry GoogLeNet, which can extract the required bandwidth information to redesign the geometric parameters of antenna without changing its physical structure. It demonstrates that the bandwidth of a reference quasi-Yagi antenna is improved from 0.6 GHz to 1.15 GHz through the proposed inverse design DL approach, and the measured bandwidth value of this redesigned quasi-Yagi antenna achieves 1.16 GHz, which is improved 93% actually. The numerical and measured results indicate that the proposed DL approach could significantly improve the performance of the existed quasi-Yagi antenna and present a new attempt to apply the image processing techniques in resolving physical problem.