Neural-network reconstruction of THz transmission spectra using electrically tunable AlGaN/GaN plasmonic-crystal analyzer

TL;DR

This paper presents a machine learning approach using neural networks to reconstruct terahertz spectra from an electrically tunable plasmonic analyzer, achieving high accuracy and outperforming traditional methods.

Contribution

It introduces a novel neural network-based reconstruction method for THz spectra using a tunable plasmonic analyzer, reducing errors and spurious peaks compared to baseline techniques.

Findings

Neural network achieves MSE of 0.015 in FTIR mode and 0.038 in direct mode.

The method correctly identifies six out of seven resonances in each mode.

Reconstruction error is reduced 3.6 times in FTIR mode compared to baseline.

Abstract

We demonstrate machine learning (ML) based reconstruction of terahertz transmission spectra using an electrically tunable grating-gate AlGaN/GaN plasmonic-crystal analyzer. The analyzer encodes the transmission spectrum into a voltage-dependent intensity, which is then inverted by an ML algorithm. A feedforward neural network trained on a synthetic dataset is validated experimentally on four samples in standard Fourier Transform Infrared (FTIR) mode and in direct (fixed-mirror) acquisition mode. The network achieves a mean square error (MSE) of the reconstruction of 0.015 in FTIR mode and 0.038 in direct mode, correctly identifying six out of seven ground-truth resonances in each mode. Against a first-difference Tikhonov regularization baseline, the mean reconstruction error is reduced 3.6 times in FTIR mode and 1.55 times in direct mode, with fewer spurious peaks and lower peak-position errors. Voltage-tunable plasmonic filtering combined with neural-network inversion establishes an interferometer-free architecture for THz spectral reconstruction.