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.

摘要

这项研究调查了人工神经网络的应用，以确定从实验 VNA 散射参数测量得出的复杂介电材料特性。该研究利用有限元方法综合生成数据来训练神经网络。神经网络使用监督学习方法进行训练，并使用实验测量数据进行验证。感兴趣的频率范围在 0.1 到 13.5 GHz 之间，介电常数的实部范围为 1 - 100，虚部范围为 0 - 0.2。与现有的逆向方法相比，这种建模方法降低了不确定性。这种方法展示了一个通用框架，可用于将实验或计算导出的散射参数转换为复介电常数。