Miniaturized infrared spectrometer based on the tunable graphene plasmonic filter

Abstract

Miniaturization of a conventional spectrometer is challenging because of the tradeoffs of size, cost, signal-to-noise ratio, and spectral resolution, etc. Here, a new type of miniaturized infrared spectrometer based on the integration of tunable graphene plasmonic filters and infrared detectors is proposed. The transmittance spectrum of a graphene plasmonic filter can be tuned by varying the Fermi energy of the graphene, allowing light incident on the graphene plasmonic filter to be dynamically modulated in a way that encodes its spectral information in the receiving infrared detector. The incident spectrum can then be reconstructed by using decoding algorithms such as ridge regression and neural networks. The factors that influence spectrometer performance are investigated in detail. It is found that the graphene carrier mobility and the signal-to-noise ratio are two key parameters in determining the resolution and precision of the spectrum reconstruction. The mechanism behind our observations can be well understood in the framework of the Wiener deconvolution theory. Moreover, a hybrid decoding (or recovery) algorithm that combines ridge regression and a neural network is proposed that demonstrates a better spectral recovery performance than either the ridge regression or a deep neural network alone, being able to achieve a sub-hundred nanometer spectral resolution across the 8∼14 µm wavelength range. The size of the proposed spectrometer is comparable to a microchip and has the potential to be integrated within portable devices for infrared spectral imaging applications.