Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave

Abstract

In this paper, we introduce inverse design of nearly arbitrary planar antenna structures with a deep convolutional neural network (CNN) modeling that allows rapid and accurate prediction of antenna performance (scattering parameters and radiation patterns). Quite distinct from prior efforts of ML-based antennas with fixed template geometries and finite degrees of freedom, this approach of generalizing to arbitrary planar structures opens up a new design space for antenna structures with properties beyond what can be achieved with antennas optimized from a finite library. By eliminating complex time consuming electromagnetic simulations with an ML-based approach, we propose an inverse design with evolutionary algorithms that allows a much larger search space than classical genetic algorithm based approaches. We demonstrate this methodology with simulation and measurement results of inverse designed compact, broadband and multi-band planar antennas operating at RF (2-5 GHz) and mmWave (20-40 GHz).