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Ultrathin and two-dimensional (2D) metals can support strong plasmons, with concomitant tight field confinement and large field enhancement. Accordingly, 2D-metal nanostructures exhibiting plasmonic resonances are highly sensitive to the environment and intrinsically suitable for optical sensing. Here, based on a proof-of-concept numerical study, nano-engineered ultrathin 2D-metal films that support infrared plasmons are demonstrated to enable highly responsive refractive index (RI) sensing. For 3 nm-Au nanoribbons exhibiting plasmonic resonances at wavelengths around 1600 nm, a RI sensitivity of SRI > 650 nm per refractive index unit (RIU) is observed for a 100 nm-thick analyte layer. A parametric study of the 2D-Au system indicates the strong dependence of the RI sensitivity on the 2D-metal thickness. Furthermore, for an analyte layer as thin as 1 nm, a RI sensitivity up to 110 (90 nm/RIU) is observed in atomically thin 2D-In (2D-Ga) nanoribbons exhibiting highly localized plasmonic resonances at mid-infrared wavelengths. Our results not only reveal the extraordinary sensing characteristics of 2D-metal systems but also provide insight into the development of 2D-metal-based plasmonic devices for enhanced IR detection.

 

Abstract Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model, as the weights are represented by probability distributions (e.g. Gaussian distribution). Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve Gaussian random number generator-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator for a data classification task.