| Issue |
EPJ Web Conf.
Volume 266, 2022
EOS Annual Meeting (EOSAM 2022)
|
|
|---|---|---|
| Article Number | 13019 | |
| Number of page(s) | 2 | |
| Section | Topical Meeting (TOM) 13- Advances and Applications of Optics and Photonics | |
| DOI | https://doi.org/10.1051/epjconf/202226613019 | |
| Published online | 13 October 2022 | |
https://doi.org/10.1051/epjconf/202226613019
Extracting complex refractive indices from THz-TDS data with artificial neural networks
1 School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK
2 School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
3 Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK
* e-mail: n.t.klokkou@soton.ac.uk
Published online: 13 October 2022
Terahertz time-domain spectroscopy (THz-TDS) benefits from high signal-to-noise ratios (SNR), however extraction of material parameters involves a number of steps which can introduce errors into the final result. We present the use of artificial neural networks (ANN) as the first step to achieve a comprehensive approach for the extraction of the complex refractive index from THz-TDS data. The ANN shows performance superior to approximation methods and has a more straightforward implementation than root finding methods. Deep and convolutional neural networks are demonstrated to accept an entire frequency range at once, providing a tool for fitting where SNR is low, producing a more stable result.
© The Authors, published by EDP Sciences
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