Terahertz Time-Domain Spectroscopy Materials Parameters Extraction Using Neural Networks

In this work, we present a novel approach to determine the refractive index n and the extinction coefficient κ from terahertz time-domain spectroscopy (THz-TDS) measurements using artificial neural networks (ANNs). We train the network in our experimental datasets to create a model that can accurately and efficiently calculate these material properties, the proposed neural network is superior to traditional analytical methods, which rely on approximations, and a faster and easier alternative to iterative root-finding algorithms. Our results demonstrate that this machine learning methodology solves common issues in THz-TDS data analysis, such as phase unwrapping, time-domain windowing, low computation rates, and high accuracy at low-frequency regions, effectively.