A Tunable Graphene Superlattice with Deformable Periodical Nano-Gating.

Graphene superlattices have simple and controllable electronic band structures, which can also be electrostatically tuned. They have been widely studied for band engineering and strong correlated physics, and have led to the discovery of a variety of exciting phenomena. To experimentally study the physics of graphene superlattices in a systematic way, it is desirable to control the structure parameters, which barely exist at the moment, onsite. Here, a tunable superlattice with graphene and a deformable gating structure is demonstrated. The period and duty cycle of the nano-gating, and furthermore of the superlattice potential, can be tuned through altering the shape of the gating structure with piezo-actuators, offering a tunable band structure. The tuning of the electronic band structures of both a two-dimensional and a one-dimensional superlattice is demonstrated with numerical simulations, offering a new approach for tunable electronic and photonic devices.

Miniaturized infrared spectrometer based on the tunable graphene plasmonic filter.

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.