Hexagonal boron nitride (hBN) is drawing increasing attention as an insulator and substrate material to develop next generation graphene-based electronic devices. In this paper, we investigate the quantum transport in heterostructures consisting of a
few atomic layers thick hBN film sandwiched between graphene nanoribbon electrodes. We show a gate-controllable vertical transistor exhibiting strong negative differential resistance (NDR) effect with multiple resonant peaks, which stay pronounced for various device dimensions. We find two distinct mechanisms that are responsible for NDR, depending on the gate and applied biases, in the same device. The origin of first mechanism is a Fabry-Pe like interference and that of the second mechanism is an in-plane wave vector matching when the Dirac points of the electrodes align. The hBN layers can induce an asymmetry in the current-voltage characteristics which can be further modulated by an applied bias. We find that the electron-phonon scattering introduces the decoherence and therefore suppresses first mechanism whereas second mechanism remains relatively unaffected. We also show that the NDR features are tunable by varying device dimensions. The NDR feature with multiple resonant peaks, combined with the ultrafast tunneling speed provides prospect for the graphene-hBN-graphene heterostructure in the high-performance electronics.
Modeling nanoscale devices quantum mechanically is a computationally challenging problem where new methods to solve the underlying equations are in a dire need. In this paper, we present an approach to calculate the charge density in nanoscale device
s, within the context of the non equilibrium Greens function approach. Our approach exploits recent advances in using an established graph partitioning approach. The developed method has the capability to handle open boundary conditions that are represented by full self energy matrices required for realistic modeling of nanoscale devices. Our method to calculate the electron density has a reduced complexity compared to the established recursive Greens function approach. As an example, we apply our algorithm to a quantum well superlattice and a carbon nanotube, which are represented by a continuum and tight binding Hamiltonian respectively, and demonstrate significant speed up over the recursive method.
