Graphene-Based Computing: Nanoribbon Logic Gates a Circuits

Abstract

As CMOS feature size is reaching atomic dimensions, unjustifiable static power, reliability, and economic implications are exacerbating, thus prompting for research on new materials, devices, and/or computation paradigms. Within this context, Graphene Nanoribbons (GNRs), owing to graphene’s excellent electronic properties, may serve as basic structures for carbon-based nanoelectronics. However, the graphene intrinsic energy bandgap absence hinders GNR-based devices and circuits implementation. As a result, en route to graphene-based logic circuits, finding a way to open a sizable energy bandgap, externally control GNR’s conduction, and construct reliable high-performance graphene-based gates are the main desideratum. To this end, first, we propose a GNR-based structure (building block) by extending it with additional top gates and back gate while considering five GNR shapes with zigzag edges in order to open a sizeable bandgap, and further investigate GNR geometry and contact topology influence on its conductance and current characteristics. Second, we present a methodology of encoding the desired Boolean logic transfer function into the GNR electrical characteristics, i.e., conduction maps, and then evaluate the effect of VDD variation on GNR conductance. Moreover, we find a proper external electric mean (e.g., top gates and back gates) to control the GNR behavior. Third, we develop a parameterized Verilog-A SPICEcompatible GNR model based on Non-Equilibrium Green’s Function (NEGF)-Landauer formalism that builds upon an accurate physics formalization, which enables to symbiotically exploit accurate physics results from Matlab Simulink and optimized SPICE circuit solvers (e.g., Spectre, HSPICE). Subsequently, we construct graphene-based Boolean gates by means of two complementary GNRs, and design a GNR-based 1-bit Full Adder and a SRAM cell. Finally, we extend the NEGF-Landauer simulation framework with the self-consistent Born approximation while taking into account the temperature-induced phenomena in GNR electron transport, i.e., electron-phonon interactions for both optical and acoustic phonons, and further explore the graphene-based gates performance robustness under temperature variations.