Effect of geometrical structure on transport properties of silicene nanoconstrictions*

We study electrical modulation of transport properties of silicene nanoconstrictions with different geometrical structures. We investigate the effects of the position and width of the central scattering region on the conductance with increasing Fermi energy. It is found that the conductance significantly depends on the position and the width of the nanoconstriction. Interestingly, the symmetrical structure of the central constriction region can induce a resonance effect and significantly increase the systemʼs conductance. We also propose a novel two-channel structure with an excellent performance on the conductance compared to the one-channel structure with the same total width. Such geometrically-induced conductance modulation of silicene nanostructures can be achieved in practice via current nanofabrication technology.

Ultrafast Switching and Linear Conductance Modulation in Ferroelectric Tunnel Junctions via P(VDF-TrFE) Morphology Control

Neuromorphic computing architectures demand development of analog, non-volatile memory components operating at femto-Joule/bit operation energy. Electronic components working at this energy range require devices operating at ultrafast timescales. Among different...

Memristive and Synaptic Characteristics of Nitride-Based Heterostructures on Si Substrate

Brain-inspired artificial synaptic devices and neurons have the potential for application in future neuromorphic computing as they consume low energy. In this study, the memristive switching characteristics of a nitride-based device with two amorphous layers (SiN/BN) is investigated. We demonstrate the coexistence of filamentary (abrupt) and interface (homogeneous) switching of Ni/SiN/BN/n++-Si devices. A better gradual conductance modulation is achieved for interface-type switching as compared with filamentary switching for an artificial synaptic device using appropriate voltage pulse stimulations. The improved classification accuracy for the interface switching (85.6%) is confirmed and compared to the accuracy of the filamentary switching mode (75.1%) by a three-layer neural network (784 × 128 × 10). Furthermore, the spike-timing-dependent plasticity characteristics of the synaptic device are also demonstrated. The results indicate the possibility of achieving an artificial synapse with a bilayer SiN/BN structure.