| Issue |
EPJ Web Conf.
Volume 335, 2025
EOS Annual Meeting (EOSAM 2025)
|
|
|---|---|---|
| Article Number | 03046 | |
| Number of page(s) | 2 | |
| Section | Topical Meeting - Applications of Optics and Photonics | |
| DOI | https://doi.org/10.1051/epjconf/202533503046 | |
| Published online | 22 September 2025 | |
https://doi.org/10.1051/epjconf/202533503046
Terahertz Time-Domain Spectroscopy Materials Parameters Extraction Using Neural Networks
Laboratoire Systèmes Lasers, Ecole Militaire Polytechnique, Algiers, Algeria
Published online: 22 September 2025
Abstract
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.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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