THz-TDS: extracting complex conductivity of two-dimensional materials via neural networks trained on synthetic and experimental data

Abstract

Terahertz time-domain spectroscopy (TDS) has proved immensely useful for probing 2D materials such as graphene. Unlike in the visible regime, the optical properties at terahertz frequencies are highly dependant on charge carrier mobility and scattering time. However, extracting the material properties from the terahertz waveform is a non-trivial process, which can be prone to producing erroneous results. Artificial neural networks have recently been demonstrated as useful tools to extract complex refractive index from terahertz time domain data. Here, we propose the use of artificial neural networks to interpret terahertz spectra of graphene monolayers to extract the charge carrier mobility and scattering time. We demonstrate improved performance on out-of-distribution data by using a combination of synthetically generated spectra and experimental data during training.

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