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Electrostatically doped graphene p–n junctions can modulate graphene nanoribbon conductance and can be indispensable parts of nanoelectronic graphene circuits. Much research has been conducted on such devices with one rectangular top gate and one back gate with very encouraging results. Recently, graphene p–n junctions with two rectangular top gates have been proposed and their study revealed a rich behaviour that allows their use in both analogue and digital nanoelectronic circuits. Here we study graphene p–n junctions with two trapezoid top gates and a common back gate with special focus to the effect of the angle and the distance between the two top gates. Furthermore, trapezoid top gates with angles of 45° make use of the Veselago lens effect, allowing an effective control by tuning density of states and carrier density. We simulated these devises using the non-equilibrium Green’s function method combined with tight-binding Hamiltonians in the ballistic transport limit. Our results show that the conductance of these graphene p–n junctions can be successfully controlled by various combinations of different parameters that allows for realisation of carbon-based logic gates. We also present the design and simulation a universal set Boolean gates, namely NOT, NAND and NOR.

Metal-Insulator-Metal type memristors as emergent nano-electronic devices have been successfully fabricated and used in non-conventional and neuromorphic computing systems in the last years. Several behavioral or physical based models have been developed to explain their operation and to optimize their fabrication parameters. Among them, the resistance switching of the insulating layer due to the formation of conductive filaments is the most well respected and experimentally proven. All existing memristor models are trade-offs between accuracy, universality and realism, but, to the best of our knowledge, none of them is purely characterized as quantum mechanical, despite the fact that quantum mechanical processes are a major part of the memristor operation. In this paper, we employ quantum mechanical methods to develop a complete and accurate filamentary model for the resistance variation during memristor's operating cycle. More specifically, we apply quantum walks to model and compute the motion of atoms forming the filament, tight-binding Hamiltonians to capture the filament structure and the Non-Equilibrium Green's Function (NEGF) method to compute the conductance of the device. Furthermore, we proceeded with the parallelization of the overall model through Graphical Processing Units (GPUs) to accelerate our computations and enhance the model's performance adequately. Our simulation results successfully reproduce the resistive switching characteristics of memristors devices, match with existing fabricated devices experimental data, prove the efficacy and robustness of the proposed model in terms of multi-parameterization, and provide a new and useful insight into its operation.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Graphene nanoribbons (GNRs) are the most important emerging Graphene structures for nanoelectronic and sensor applications. GNRs with perfect lattices have been extensively studied, but fabricated GNRs contain lattice defects the effect of which on their electronic properties has not been studied extensively enough. In this paper, we apply the Non-Equilibrium Green's function (NEGF) method combined with tight-binding Hamiltonians to investigate the effect of lattice defects on the conductance of GNRs. We specifically study, butterfly shaped GNRs, which operate effectively as switches, and have been used in CMOS-like architectures. The cases of the most usual defects, namely the single and double vacancy have been analytically examined. The effect of these vacancies was computed by placing them in different regions and with various numbers on GNR nano-devices, namely edges, main body, contacts and narrow regions. The computation results are presented in the form of energy dispersion diagrams as well as diagrams of maximum conductance as a function of the number of lattice defects. We also present results on the defect tolerance of the butterfly shaped GNR devices.

Graphene nanoribbons with non-magnetic contacts have been extensively studied and are widely used in graphene based devices. In these devices, the nanoribbon conductance is modulated and controlled by applying potentials through top and back gates. Graphene nanoribbons with magnetic contacts have been studied and used as spin valves, mostly at liquid-helium temperatures. The operation of graphene nanoribbons with magnetic contacts at room temperature, where nanoelectronic devices and circuits are expected to operate, has received little attention. Here we study and simulate the operation of graphene nanoribbons with magnetic contacts using tight-binding Hamiltonians and the non-equilibrium Green's functions(NEGF) method, which we extend to incorporate the effect of magnetic contacts. Our results are in very good agreement with experimental measurements of the conductance of graphene nanoribbons with NiFe ferromagnetic contacts. We also perform extensive parametric analysis of the dependence of the nanoribbon conductance on the relative orientations of the contact magnetic polarizations, combined with potentials applied through top and back gates. Our results revealed a very rich parametric space that can be exploited to develop multifunctional platforms which, among others, can be used for digital, analog and neuromorphic computations.

Signal synchronization of large scale Quantum-dot Cellular Automata (QCA) circuits is one of the most complex QCA design challenges. More specifically, the QCA circuits synchronization problem, especially in the large circuits, is characterized as rather complex due to technology constraints. In this paper, by extensively analyzing the most important properties of the signal synchronization problem in QCA circuits, we propose an efficient design methodology to tackle the problem, based on the well-known from computer science, Firing Squad Synchronization Problem (FSSP). Comparing FSSP with the QCA circuits synchronization problem many similarities can be found. Among the numerous FSSP's algorithmic solutions in literature, the Mazoyer algorithm has proven to be the most efficient one. In this paper, a novel design and implementation in QCA technology of this algorithm is presented. Moreover, by the appropriate modification of the Mazoyer algorithm, we are able to propose a generic synchronization design methodology for QCA circuits and systems. This method is enhanced by a novel freezing technique, that makes it applicable to any QCA circuit and system as manifested by our corresponding simulation results. The proposed synchronization methodology is a universal design tool, that can be applied to exiting designs without increasing the complexity.

Graphene is a 2D material with spectacular properties. Its elasticity and biocompatibility make it appropriate for stretchable electronics and bioelectronics applications respectfully. In this work, we present the design of a Graphene Nanoribbon (GNR) based XOR gate and a 3-input XOR gate, extending the universal set of AND, NOT, OR gates already presented in literature, and verifying their operation by computing its conductance. Our proposed topology allows XOR gate scaling from 2-inputs to 3-inputs with zero increase in active area. Moreover, it operates when deposited on a wide range of substrates, due to the lack of back gate bias, enabling its possible use in stretchable and biocompatible electronics applications. © 2022 IEEE.