The human brain is a natural high-performance computing systemwith outstanding properties, e.g., ultra-low energy consumption, highly parallel information processing, suitability for solving complex tasks, and robustness. As such, numerous attempts have been made to devise neuromorphic systems able to achieve brain-akin computation abilities, which can aid in understanding the complex human brain functionality and can be utilized to solve complex problems, e.g., pattern recognition and data mining. However, the fact that human brain comprises billions of neurons, which are the fundamental information processing units, and trillions of synapses that interconnect them makes the design and implementation of large-scale brain-inspired computing systems quite a challenging task. Graphene appears to be a promising candidate for scalable neuromorphic implementations as it exhibits a wealth of outstanding properties, e.g., ballistic transport, ultimate thinness, flexibility, and graphene devices are capable of emulating complex nonlinear functions and can be readily tuned to provide various conduction dynamicswhile preserving low energy operation and small footprint. Moreover, graphene is biocompatible, which offers perspectives for graphene-based neuromorphic bio-interfaces. This thesis aims to investigate graphene’s potential to enable scalable and energy effective neuromorphic computing. To this end, we first introduce an atomistic-level simulation model for calculating graphene electronic transport properties, that captures the hysteresis effects induced by interface charges trapping/detrapping phenomena. Second, we propose a generic graphene based synapse, which can be tailored to emulate different synaptic plasticity types by properly modifying its Graphene NanoRibbon (GNR) shape and contacts topology, as well as applying external voltages. Subsequently, we introduce a compact graphene-based integrate-and-fire spiking neuron that mimics the basic spiking neuronal dynamics. We further propose a basic SpikingNeuralNetwork (SNN) unit,which can be utilized to implement complex graphene-based SNNstructures. Finally,we introduce a reconfigurable graphene-based SNN architecture and a training methodology for obtaining the initial SNN synaptic weight values. We demonstrate the feasibility of the synaptic weights training methodology and the practical capabilities of the proposedSNNarchitecture by applying them to solve character recognition and edge detection problems. Our experiments clearly indicate that the proposed graphene-based neuromorphic approach enables lowenergy operation at small chip real estate footprint, which are enabling factors for the realization of scalable energy-efficient SNN implementations. @en

Design and implementation of artificial neuromorphic systems able to provide brain akin computation and/or bio-compatible interfacing ability are crucial for understanding the human brain's complex functionality and unleashing brain-inspired computation's full potential. To this end, the realization of energy-efficient, low-area, and bio-compatible artificial synapses, which sustain the signal transmission between neurons, is of particular interest for any large-scale neuromorphic system. Graphene is a prime candidate material with excellent electronic properties, atomic dimensions, and low-energy envelope perspectives, which was already proven effective for logic gates implementations. Furthermore, distinct from any other materials used in current artificial synapse implementations, graphene is biocompatible, which offers perspectives for neural interfaces. In view of this, we investigate the feasibility of graphene-based synapses to emulate various synaptic plasticity behaviors and look into their potential area and energy consumption for large-scale implementations. In this article, we propose a generic graphene-based synapse structure, which can emulate the fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity. Additionally, the graphene synapse is programable by means of back-gate bias voltage and can exhibit both excitatory or inhibitory behavior. We investigate its capability to obtain different potentiation/depression time scale for STDP with identical synaptic weight change amplitude when the input spike duration varies. Our simulation results, for various synaptic plasticities, indicate that a maximum 30% synaptic weight change and potentiation/depression time scale range from [-1.5 ms, 1.1 ms to [-32.2 ms, 24.1 ms] are achievable. We further explore the effect of our proposal at the Spiking Neural Network (SNN) level by performing NEST-based simulations of a small SNN implemented with 5 leaky-integrate-and-fire neurons connected via graphene-based synapses. Our experiments indicate that the number of SNN firing events exhibits a strong connection with the synaptic plasticity type, and monotonously varies with respect to the input spike frequency. Moreover, for graphene-based Hebbian STDP and spike duration of 20ms we obtain an SNN behavior relatively similar with the one provided by the same SNN with biological STDP. The proposed graphene-based synapse requires a small area (max. 30 nm2), operates at low voltage (200 mV), and can emulate various plasticity types, which makes it an outstanding candidate for implementing large-scale brain-inspired computation systems.

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In the paper we propose a reconfigurable graphene-based Spiking Neural Network (SNN) architecture and a training methodology for initial synaptic weight values determination. The proposed graphene-based platform is flexible, comprising a programmable synaptic array which can be configured for different initial synaptic weights and plasticity functionalities and a spiking neuronal array, onto which various neural network structures can be mapped according to the application requirements and constraints. To demonstrate the validity of the synaptic weights training methodology and the suitability of the proposed SNN architecture for practical utilization, we consider character recognition and edge detection applications. In each case, the graphene-based platform is configured as per the application tailored SNN topology and initial state and SPICE simulated to evaluate its reaction to the applied input stimuli. For the first application, a 2-layer SNN is used to perform character recognition for 5 vowels. Our simulation indicates that the graphene-based SNN can achieve comparable recognition accuracy with the one delivered by a functionally equivalent Artificial Neural Network. Further, we reconfigure the architecture for a 3-layer SNN to perform edge detection on 2 grayscale images. SPICE simulation results indicate that the edge extraction results are close agreement with the one produced by classical edge detection operators. Our results suggest the feasibility and flexibility of the proposed approach for various application purposes. Moreover, the utilized graphene-based synapses and neurons operate at low supply voltage, consume low energy per spike, and exhibit small footprints, which are desired properties for largescale energy-efficient implementations.@en

Designing and implementing artificial systems that can be interfaced with the human brain or that can provide computational ability akin to brain's processing information efficient style is crucial for understanding human brain fundamental operating principles and to unleashing the full potential of brain-inspired computing. As basic neural network components, responsible for information transfer between neurons, artificial synapses able to emulate analog biological synaptic behaviour are of particular interest. State of the art CMOS and memristor-based synapses suffer from scalability drawbacks (large energy consumption and area footprint), variability-induced instability, and are not bio-compatible. In this paper, we propose a generic Graphene Nanoribbon (GNR) based synapse structure and demonstrate that by changing GNR geometry and external bias voltages it can emulate different synaptic plasticity behaviours, i.e., Spike Timing Dependent Plasticity and LongTerm Depression and Potentiation, and that both excitatory and inhibitory synaptic behavior can be obtained with the same GNR geometry. To demonstrate biologically plausible operation, we make use of low voltage bias, i.e., 0.1V, 0.2 V, and consider inputs consistent with measured brain synapses data, i.e.,-50 mV to 50 mV pre-and post-synaptic spikes voltage range, and-60ms to 60 ms time range. The simulations indicate that by changing the GNR shape we can enrich the plasticity behaviour (potentially beyond the considered cases) and the plasticity change of 100% provided by natural synapses can be achieved. Our investigation clearly suggests that the proposed GNR synapse structure is a promising candidate for large-scale neuromorphic systems integration, which might potentially bring novel insight on brain neurophysiology, as it requires a small footprint, is energy effective, biocompatible, and versatile from the synaptic behaviour point of view.

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Designing and implementing artificial neuromorphic systems, which can provide biocompatible interfacing, or the human brain akin ability to efficiently process information, is paramount to the understanding of the human brain complex functionality. Energy-efficient, low-area, and biocompatible artificial neurons are key ubiquitous components of any large scale neural systems. Previous CMOS-based neurons implementations suffer from scalability drawbacks and cannot naturally mimic the analog behavior. Memristor and phase-changed neurons have variability-induced instability drawbacks, and usually rely on additional CMOS circuitry. However, graphene, despite its ballistic transport, inherently analog nature, and biocompatibility, which provide natural support for biologically plausible neuron implementations has only been considered for Boolean logic implementations. In this paper, we propose an ultra-compact, all graphene-based nonlinear Leaky Integrate-and-Fire spiking neuron. By means of SPICE simulations, we validate its basic functionality and investigate the output spikes response under stochastic noisy input spike trains with a variable firing rate, from 20 to 200 spikes per second. Simulation results indicate neuron robustness to noisy scenarios, and neuronal output firing regularity. The small area and the low energy consumption, due to 200mV supply voltage operation, can benefit the implementation of large scale neural networks, and the biologically plausible operating conditions (e.g., 2ms and 100mV spike duration and amplitude), can promote the interfacebility of graphene-based artificial neurons with biological counterparts. @en

As CMOS scaling is reaching its limits, high power density and leakage, low reliability, and increasing IC production costs are prompting for developing new materials, devices, architectures, and computation paradigms. Additionally, temperature variations have a significant impact on devices and circuits reliability and performance. Graphene's remarkable properties make it a promising post Silicon frontrunner for carbon-based nanoelectronics. While for CMOS gates temperature effects have been largely investigated, for gates implemented with atomic-level Graphene Nanoribbons (GNRs), such effects have not been explored. This paper presents the results of such an analysis performed on a set of GNR-based Boolean gates by varying the operation temperature within the military range, i.e., -55°C to 125°C, and evaluating by means of SPICE simulations gate output signal integrity, propagation delay, and power consumption. Our simulation results reveal that GNR-based gates are robust with respect to temperature variation, e.g., 5.2% and 5.3% maximum variations of NAND output logic '1' (VOH) and logic '0' ($V$OL) voltage levels, respectively. Moreover, even in the worst condition GNR-based gates outperform CMOS FinFET 7nm counterparts, e.g., 1.6× smaller delay and 185× less power consumption for the INV case, which is strengthening their great potential as basic building blocks for future reliable, low-power, nanoscale carbon-based electronics.

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As CMOS feature size is reaching atomic dimensions, unjustifiable static power, reliability, and economic implications are exacerbating, thereby prompting for conducting 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. In this paper, we make use of the fact that GNR behavior can be modulated via top/back gate contacts to mimic a given functionality and combine complementary GNRs for constructing Boolean gates. We first introduce a generic gate structure composed of a pull-up GNR performing the gate Boolean function and a pull-down GNR performing its complement. Then, we seek GNR dimensions and gate topologies required for the design of 1-, 2-, and 3-input graphene-based Boolean gates, validate the proposed gates by means of SPICE simulation, which makes use of a non-equilibrium Green's function Landauer formalism based Verilog-A model to calculate GNR conductance, and evaluate their performance with respect to propagation delay, power consumption, and active area footprint. Simulation results indicate that, when compared with 7 nm FinFET CMOS counterparts, the proposed gates exhibit 6 × to 2 orders of magnitude smaller propagation delay, 2 to 3 orders of magnitude lower power consumption, and necessitate 2 orders of magnitude smaller active area footprint. We further present full adder (FA) and SRAM cell GNR designs, as they are currently fundamental components for the construction of any computation system. For an effective FA implementation, we introduce a 3-input MAJORITY gate, which apart of being able to directly compute FA's carry-out is an essential element in the implementation of error correcting codes codecs, which outperforms the CMOS equivalent carry-out calculation circuit by 2 and 3 orders of magnitude in terms of delay and power consumption, respectively, while requiring 2 orders of magnitude less area. The proposed FA exhibits 6.2 × smaller delay, 3 orders of magnitude less power consumption, while requiring 2 orders of magnitude less area, when compared with the 7 nm FinFET CMOS counterpart. However, because of the effective carry-out circuitry, a GNR-based n-bit ripple carry adder, whose performance is linear in the carry-out path, will be 108 × faster than an equivalent CMOS implementation. The GNR-based SRAM cell provides a slightly better resilience to dc-noise characteristics, while performance-wise has a 3.6 × smaller delay, consumes 2 orders of magnitude less power, and requires 1 order of magnitude less area than the CMOS equivalent. These results clearly indicate that the proposed GNR-based approach is opening a promising avenue toward future competitive carbon-based nanoelectronics.

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Hysteretic behavior has been experimentally observed in graphene-based structures and has a major influence on graphene surface potential and gate field modulation ability. Thus, a graphene electronic transport modelling methodology, which incorporates hysteresis effects is crucial in order to properly assess gated-controlled graphene structures response and performance. To this end, we propose an atomistic-level electronic transport model, which is non restricted to rectangular graphene geometries and captures hysteretic effects caused by near-interfacial traps, provided that interface traps trapping/detrapping time constant and density are known. We apply the model on a rectangular graphene shape and validate our results against experimentally measured drain current vs. top gate voltage hysteresis curves. Moreover, to demonstrate model's versatility we consider two non-rectangular Graphene NanoRibbons (GNRs) and investigate their hysteresis behaviour. Our experiments indicate good agreement between simulated and measured results, which qualifies the model appropriate for traps-aware exploration of the conduction behaviour of graphene-based devices and circuits.

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