Data Driven Surrogate Modeling of Phase Array Antennas Using Deep Learning for Millimetric Band Applications

Abstract

Phased Array Antenna (PAA) technology plays an important role in fields such as radar, 5G and satellite or any application which requires wide bandwidth and high gain. However, achieving such design is a difficult and complex task that requires an accurate calculation and combination of results obtained for varying phase and amplitude of each unit and coupling effects between these elements of the PAA structure is a task that can only be obtained using full wave EM simulation tools. This comes at the price of a significant increase for the computational cost of the design process which is a well-known drawback of forward EM modeling of microwave stages most especially in case of repetitive analysis's such as yield analyses or optimization tasks. Data-driven surrogate models have emerged as a powerful and versatile solution that bridges the gap between computationally expensive simulations and rapid, reliable prediction models suitable for deployment in applications such as optimization and/or yield analyses. Herein, for having a high-performance broadband PAA for millimeter band in a computationally efficient manner, artificial intelligence based surrogate model assisted optimization approach is deployed. A series of state-of-the-art surrogate modeling algorithms are deployed to create a surrogate model of the studied PAA design for the prediction of radiation pattern characteristic with respect to the input phase values of each array element. As a result, a drastic reduction in computational time of almost 90% for the optimization of three PAA designs is achieved. Thus, the proposed approach offers promising avenues for further exploration in computational electromagnetics, most especially in simulation expensive problems with complex designs.