Artificial neural network application for novel 3D printed nonuniform ceramic reflectarray antenna

Abstract

The main inconvenience in design process of modern high performance reflectarray antennas is that these designs are heavily depended on full-wave electromagnetic simulation tools, where in most of the cases the design optimization process would be an inefficient or impractical. However, thanks to the recent advances in computer-aided design and advanced hardware systems, artificial neural networks based modeling of microwave systems has become a popular research topic. Herein, design optimization of an alumina-based ceramic substrate reflectarray antenna by using multilayer perceptron (MLP) and 3D printing technology had been presented. MLP-based model of ceramic reflectarray (CRA) unit element is used as a fast, accurate, and reliable surrogated model for the prediction of reflection phase of the incoming EM wave on the CRA unit cell with respect to the variation of unit elements design parameters, operation frequency, and substrate thickness. The structural design of a reflectarray antenna with nonuniform reflector height operating in X band has been fabricated for the experimental measurement of reflectarray performance using 3D printer technology. The horn feeding based CRA antenna has a measured gain characteristic of 22 dBi. The performance of the prototyped CRA antenna is compared with the counterpart reflectarray antenna designs in the literature.