Application of Artificial Neural Networks to Complex Dielectric Constant Estimation from Free-Space Measurements

Abstract

Adequate characterization of materials allows the engineer to select the best option for each application. Apart from mechanical or environmental characterization, last decades' rise in the exploitation of the electromagnetic spectrum has made increasingly important to understand and explain the behavior of materials also in that ambit. The electromagnetic properties of non-magnetic materials are governed by their intrinsic permittivity or dielectric constant and free-space measurements is one of the various methods employed to estimate this quantity at microwave frequencies. This paper proposes the application of Artificial Neural Networks (ANNs) to extract the dielectric constant of materials from the reflection coefficient obtained by free-space measurements. In this context, two kind of ANNs are examined: Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks. Simulated materials are utilized to train the networks with and without noise and performance is tested using an actual material sample measured by the authors in an anechoic chamber.