Presentation + Paper
15 November 2024 AI-assisted ultrafast and efficient terahertz spectrometer using frequency selective surfaces
Rejeena Sebastian, R. Ahmad, J. Lafrenière-Greig, X. Ropagnol, F. Blanchard
Author Affiliations +
Abstract
We introduce a novel method for ultrafast selective multispectral terahertz (THz) spectroscopy, combining broadband THz pulses, Frequency Selective Surfaces (FSS), and a Schottky diode energy sensor. Traditional THz spectroscopy is costly time-consuming and expert-operated. Our system answers to these challenges by not requiring to obtaining the time trace of the electric field of the THz signal thus essentially simplifying the system. Our system efficiently identifies samples by analyzing distinct spectral signatures. Experimental results demonstrate the method's ability to distinguish samples with similar THz absorption coefficients and refractive indices, even without clear fingerprint features. Validation on paper samples with closely matched THz properties confirmed successful differentiation through data averaging and normalization. We also applied k-fold cross-validation with a neural network for multi-class classification, achieving a training accuracy of 94.5% and an average testing accuracy of 94%. This approach offers robust real-time spectroscopic identification and potential for industrial applications and predictive modelling of THz signals.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Rejeena Sebastian, R. Ahmad, J. Lafrenière-Greig, X. Ropagnol, and F. Blanchard "AI-assisted ultrafast and efficient terahertz spectrometer using frequency selective surfaces", Proc. SPIE 13203, Sensors and Communication Technologies in the 1 GHz to 10 THz Band, 132030A (15 November 2024); https://doi.org/10.1117/12.3030981
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KEYWORDS
Terahertz radiation

Spectroscopy

Bandpass filters

Tunable filters

Education and training

FSS based metamaterials

Transmittance

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