Abstract
This study investigates the application of artificial neural networks to determine the complex dielectric material properties derived from experimental VNA scattering parameter measurements. The study utilizes a finite element approach to synthetically generate data to train the neural network. The neural network was trained using a supervised learning approach and validated using experimental measurement data. The frequency range of interest was between 0.1 and 13.5 GHz with the real part of the dielectric constants ranging from 1 − 100 and the imaginary part ranging from 0 − 0.2. This modelling approach decreases the uncertainty when compared to existing inverse approaches. This approach demonstrates a general framework that can be used for converting experimental or computational derived scattering parameters to complex permittivities.
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