Artificial neural network extraction of complex conductivity of thin graphene layers using terahertz time-domain spectrometry

Beddoes, Ben, Klokkou, Nicholas, Goreck, Jon, Whelan, Patrick Rebsdorf, Bøggild, Peter, Jepsen, Peter Uhd, Kaczmarek, Malgosia and Apostolopoulos, Vasilis (2024) Artificial neural network extraction of complex conductivity of thin graphene layers using terahertz time-domain spectrometry. In 2024 49th International Conference on Infrared, Millimeter, and Terahertz Waves, IRMMW-THz 2024. IEEE Computer Society. 2 pp . (doi:10.1109/IRMMW-THz60956.2024.10697536).

Abstract

Terahertz time-domain spectroscopy (THz-TDS) has shown significant potential for characterising the electrical properties of 2D materials, including graphene, in a non-invasive manner. However, extracting material parameters is analytically complicated. Furthermore, it requires fitting a transfer function for a physical model of conductivity such as a Drude model, which in many cases does not accurately represent real world samples. Here we present a neural network trained using simulated datasets that's capable of extracting the complex conductivity of thin graphene layers from experimentally acquired data. Our end goal is to create a neural network, trained on multiple theoretical models and experimental measurements, capable of extracting electronic parameters directly from the time domain and able to classify which conductivity model represents the sample.

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Contributors

Author: Nicholas Klokkou

Author: Vasilis Apostolopoulos

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