Showing papers on "Memristor published in 2022"

Memristive Rulkov Neuron Model With Magnetic Induction Effects

Abstract: The magnetic induction effects have been emulated by various continuous memristive models but they have not been successfully described by a discrete memristive model yet. To address this issue, this article first constructs a discrete memristor and then presents a discrete memristive Rulkov (m-Rulkov) neuron model. The bifurcation routes of the m-Rulkov model are declared by detecting the eigenvalue loci. Using numerical measures, we investigate the complex dynamics shown in the m-Rulkov model, including regime transition behaviors, transient chaotic bursting regimes, and hyperchaotic firing behaviors, all of which are closely relied on the memristor parameter. Consequently, the involvement of memristor can be used to simulate the magnetic induction effects in such a discrete neuron model. Besides, we elaborate a hardware platform for implementing the m-Rulkov model and acquire diverse spiking-bursting sequences. These results show that the presented model is viable to better characterize the actual firing activities in biological neurons than the Rulkov model when biophysical memory effect is supplied.

Hidden coexisting hyperchaos of new memristive neuron model and its application in image encryption

Abstract: The neuron models have been widely applied to neuromorphic computing systems and chaotic circuits. However, discrete neuron models and their application in image encryption have not gotten a lot of attention yet. This paper first presents a novel neuron model with significant chaotic characteristics, by coupling a memristor into the proposed neuron, a memristive neuron model is further obtained. Relevant control parameter-relied dynamical evolution is demonstrated using several numerical methods . The explorations manifest that memristor can boost chaos complexity of the discrete neuron, resulting in hyperchaos, infinite coexisting hidden attractors and attractor growing. Particularly, the NIST test verifies the generated hyperchaotic sequences exhibit high complexity, which makes them applicable to many applications based on chaos. Additionally, digital experiments based on developed hardware platform are designed to implement the memristive neuron model and get the hyperchaos. We also propose a new encryption scheme to apply the memristive neuron to the application of image encryption. The evaluation results show that the conceived algorithm appears excellent security characteristics and can effectively protect the information security of images.

Volatile and Nonvolatile Memristive Devices for Neuromorphic Computing

Abstract: Ion migration as well as electron transfer and coupling in resistive switching materials endow memristors with a physically tunable conductance to resemble synapses, neurons, and their networks. Four different types of volatile memristors and another four types of nonvolatile memristors are systemically surveyed in terms of the switching mechanisms and electrical properties that are the basis of different computing applications. The volatile memristor features spontaneous conductance decay after the cease of electrical/optical stimulations, which are closely related to the surface atom diffusion, metal–insulator–transition (including charge–density–wave), thermal spontaneous emission, and charge polarization. Such unique dynamic state evolution at the edge of chaos has enabled them to emulate certain synaptic and neural dynamics, leading to various applications ranging from spiking neural networks to combinatorial optimizations. Nonvolatile resistive switching behavior originated from the electron spins, ferroelectric polarization, crystalline-amorphous transitions or interplay between ions and electrons enables the memristor array to implement the vector–matrix multiplication, which is the key convolutional operation in artificial neural networks. The progress, challenges, and opportunities for both volatile and nonvolatile memristor in the level of materials, integration technology, algorithm, and system are highlighted in this review.

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing

Abstract: Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. "Reconfigurable memristors" that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes - volatile (2 × 106 cycles) and non-volatile (5.6 × 103 cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices.

Nanostructured perovskites for nonvolatile memory devices.

Abstract: Perovskite materials have driven tremendous advances in constructing electronic devices owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties, flexible dimensionality engineering, and so on. Particularly, emerging nonvolatile memory devices (eNVMs) based on perovskites give birth to numerous traditional paradigm terminators in the fields of storage and computation. Despite significant exploration efforts being devoted to perovskite-based high-density storage and neuromorphic electronic devices, research studies on materials' dimensionality that has dominant effects on perovskite electronics' performances are paid little attention; therefore, a review from the point of view of structural morphologies of perovskites is essential for constructing perovskite-based devices. Here, recent advances of perovskite-based eNVMs (memristors and field-effect-transistors) are reviewed in terms of the dimensionality of perovskite materials and their potentialities in storage or neuromorphic computing. The corresponding material preparation methods, device structures, working mechanisms, and unique features are showcased and evaluated in detail. Furthermore, a broad spectrum of advanced technologies (e.g., hardware-based neural networks, in-sensor computing, logic operation, physical unclonable functions, and true random number generator), which are successfully achieved for perovskite-based electronics, are investigated. It is obvious that this review will provide benchmarks for designing high-quality perovskite-based electronics for application in storage, neuromorphic computing, artificial intelligence, information security, etc.

High‐Performance Neuromorphic Computing Based on Ferroelectric Synapses with Excellent Conductance Linearity and Symmetry

Abstract: Artificial synapses can boost neuromorphic computing to overcome the inherent limitations of von Neumann architecture. As a promising memristor candidate, ferroelectric tunnel junctions (FTJ) enable the authors to successfully emulate spike‐timing‐dependent synapses. However, the nonlinear and asymmetric synaptic weight update under repeated presynaptic stimulation hampers neuromorphic computing by favoring the runaway of synaptic weights during learning. Here, the authors demonstrate an FTJ whose conductivity varies linearly and symmetrically by judiciously combining ferroelectric domain switching and oxygen vacancy migration. The artificial neural network based on this FTJ‐synapse achieves classification accuracy of 96.7% during supervised learning, which is the closest to the maximum theoretical value of 98% achieved to date. This artificial synapse also demonstrates stable unsupervised learning in a noisy environment for its well‐balanced spike‐timing‐dependent plasticity response. The novel concept of controlling ionic migration in ferroelectric materials paves the way toward highly reliable and reproducible supervised and unsupervised learning strategies.

Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing

Abstract: Abstract Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability. However, memristor’s non-reliability issues have been one of the main obstacles for the development of memristor-based artificial neurons and neuromorphic computings. Here, we show a memristor 1R cross-bar array without transistor devices for individual memristor access with low variation, 100% yield, large dynamic range, and fast speed for artificial neuron and neuromorphic computing. Based on the developed memristor, we experimentally demonstrate a memristor-based neuron with leaky-integrate and fire property with excellent reliability. Furthermore, we develop a neuro-memristive computing system based on the short-term memory effect of the developed memristor for efficient processing of sequential data. Our neuro-memristive computing system successfully trains and generates bio-medical sequential data (antimicrobial peptides) while using a small number of training parameters. Our results open up the possibility of memristor-based artificial neurons and neuromorphic computing systems, which are essential for energy-efficient edge computing devices.

A locally active discrete memristor model and its application in a hyperchaotic map

Abstract: The continuous memristor is a popular topic of research in recent years, however, there is rare discussion about the discrete memristor model, especially the locally active discrete memristor model. This paper proposes a locally active discrete memristor model for the first time and proves the three fingerprints characteristics of this model according to the definition of generalized memristor. A novel hyperchaotic map is constructed by coupling the discrete memristor with a two-dimensional generalized square map. The dynamical behaviors are analyzed with attractor phase diagram, bifurcation diagram, Lyapunov exponent spectrum, and dynamic behavior distribution diagram. Numerical simulation analysis shows that there is significant improvement in the hyperchaotic area, the quasi periodic area and the chaotic complexity of the two-dimensional map when applying the locally active discrete memristor. In addition, antimonotonicity and transient chaos behaviors of system are reported. In particular, the coexisting attractors can be observed in this discrete memristive system, resulting from the different initial values of the memristor. Results of theoretical analysis are well verified with hardware experimental measurements. This paper lays a great foundation for future analysis and engineering application of the discrete memristor and relevant the study of other hyperchaotic maps.

High‐Performance Memristor Based on 2D Layered BiOI Nanosheet for Low‐Power Artificial Optoelectronic Synapses

Abstract: Artificial optoelectronic synapses with both electrical and light‐induced synaptic behaviors have recently been studied for applications in neuromorphic computing and artificial vision systems. However, the combination of visual perception and high‐performance information processing capabilities still faces challenges. In this work, the authors demonstrate a memristor based on 2D bismuth oxyiodide (BiOI) nanosheets that can exhibit bipolar resistive switching (RS) performance as well as electrical and light‐induced synaptic plasticity eminently suitable for low‐power optoelectronic synapses. The fabricated memristor exhibits high‐performance RS behaviors with a high ON/OFF ratio up to 105, an ultralow SET voltage of ≈0.05 V which is one order of magnitude lower than that of most reported memristors based on 2D materials, and low power consumption. Furthermore, the memristor demonstrates not only electrical voltage‐driven long‐term potentiation, depression plasticity, and paired‐pulse facilitation, but also light‐induced short‐ and long‐term plasticity. Moreover, the photonic synapse can be used to simulate the “learning experience” behaviors of human brain. Consequently, not only the memristor based on BiOI nanosheets shows ultra‐low SET voltage and low‐power consumption, but also the optoelectronic synapse provides new material and strategy to construct low‐power retina‐like vision sensors with functions of perceiving and processing information.

Memristor-Based Hyperchaotic Maps and Application in Auxiliary Classifier Generative Adversarial Nets

Abstract: With the nonlinearity and plasticity, memristors are widely used as nonlinear devices for chaotic oscillations or as biological synapses for neuromorphic computations. But discrete memristors (DMs) and their coupling maps have not received much attention, yet. Using a DM model, this article presents a general three-dimensional discrete memristor-based (3-D-DM) map model. By coupling the DM with four 2-D discrete maps, four examples of 3-D-DM maps with no or infinitely many fixed points are generated. We simulate the coupling coefficient-depended and memristor initial-boosted bifurcation behaviors of these 3-D-DM maps using numerical measures. The results demonstrate that the memristor can enhance the chaos complexity of existing discrete maps and its coupling maps can display hyperchaos. Furthermore, a hardware platform is developed to implement the 3-D-DM maps and the acquired hyperchaotic sequences have high randomness. Particularly, these hyperchaotic sequences can be applied to the auxiliary classifier generative adversarial nets for greatly improving the discriminator accuracy.

Memristor-Based Binarized Spiking Neural Networks: Challenges and applications

Abstract: Memristive arrays are a natural fit to implement spiking neural network (SNN) acceleration. Representing information as digital spiking events can improve noise margins and tolerance to device variability compared to analog bitline current summation approaches to multiply–accumulate (MAC) operations. Restricting neuron activations to single-bit spikes also alleviates the significant analog-to-digital converter (ADC) overhead that mixed-signal approaches have struggled to overcome. Binarized, and more generally, limited-precision, NNs are considered to trade off computational overhead with model accuracy, but unlike conventional deep learning models, SNNs do not encode information in the precision-constrained amplitude of the spike. Rather, information may be encoded in the spike time as a temporal code, in the spike frequency as a rate code, and in any number of stand-alone and combined codes. Even if activations and weights are bounded in precision, time can be thought of as continuous and provides an alternative dimension to encode information in. This article explores the challenges that face the memristor-based acceleration of NNs and how binarized SNNs (BSNNs) may offer a good fit for these emerging hardware systems.

Manipulation of current rectification in van der Waals ferroionic CuInP2S6

Abstract: Developing a single-phase self-rectifying memristor with the continuously tunable feature is structurally desirable and functionally adaptive to dynamic environmental stimuli variations, which is the pursuit of further smart memristors and neuromorphic computing. Herein, we report a van der Waals ferroelectric CuInP2S6 as a single memristor with superior continuous modulation of current and self-rectifying to different bias stimuli (sweeping speed, direction, amplitude, etc.) and external mechanical load. The synergetic contribution of controllable Cu+ ions migration and interfacial Schottky barrier is proposed to dynamically control the current flow and device performance. These outstanding sensitive features make this material possible for being superior candidate for future smart memristors with bidirectional operation mode and strong recognition to input faults and variations.

Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing

Abstract: Abstract Realization of high-density and reliable resistive random access memories based on two-dimensional semiconductors is crucial toward their development in next-generation information storage and neuromorphic computing. Here, wafer-scale integration of solution-processed two-dimensional MoS 2 memristor arrays are reported. The MoS 2 memristors achieve excellent endurance, long memory retention, low device variations, and high analog on/off ratio with linear conductance update characteristics. The two-dimensional nanosheets appear to enable a unique way to modulate switching characteristics through the inter-flake sulfur vacancies diffusion, which can be controlled by the flake size distribution. Furthermore, the MNIST handwritten digits recognition shows that the MoS 2 memristors can operate with a high accuracy of >98.02%, which demonstrates its feasibility for future analog memory applications. Finally, a monolithic three-dimensional memory cube has been demonstrated by stacking the two-dimensional MoS 2 layers, paving the way for the implementation of two memristor into high-density neuromorphic computing system.

Emerging of two-dimensional materials in novel memristor

Abstract: The rapid development of big-data analytics (BDA), internet of things (IoT) and artificial intelligent Technology (AI) demand outstanding electronic devices and systems with faster processing speed, lower power consumption, and smarter computer architecture. Memristor, as a promising Non-Volatile Memory (NVM) device, can effectively mimic biological synapse, and has been widely studied in recent years. The appearance and development of two-dimensional materials (2D material) accelerate and boost the progress of memristor systems owing to a bunch of the particularity of 2D material compared to conventional transition metal oxides (TMOs), therefore, 2D material-based memristors are called as new-generation intelligent memristors. In this review, the memristive (resistive switching) phenomena and the development of new-generation memristors are demonstrated involving graphene (GR), transition-metal dichalcogenides (TMDs) and hexagonal boron nitride (h-BN) based memristors. Moreover, the related progress of memristive mechanisms is remarked.

A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing

Abstract: Reservoir computing offers a powerful neuromorphic computing architecture for spatiotemporal signal processing. To boost the power efficiency of the hardware implementations of reservoir computing systems, analogue devices and components—including spintronic oscillators, photonic modules, nanowire networks and memristors—have been used to partially replace the elements of fully digital systems. However, the development of fully analogue reservoir computing systems remains limited. Here we report a fully analogue reservoir computing system that uses dynamic memristors for the reservoir layer and non-volatile memristors for the readout layer. The system can efficiently process spatiotemporal signals in real time with three orders of magnitude lower power consumption than digital hardware. We illustrate the capabilities of the system using temporal arrhythmia detection and spatiotemporal dynamic gesture recognition tasks, achieving accuracies of 96.6% and 97.9%, respectively. Our memristor-based fully analogue reservoir computing system could be of use in edge computing applications that require extremely low power and hardware cost. Dynamic and non-volatile memristors can be used to create hardware-based reservoir and readout layers in artificial neural networks, providing a fully analogue signal processing chain for efficient data classification.

Metal-containing organic compounds for memory and data storage applications.

Abstract: With the upcoming trend of Big Data era, some new types of memory technologies have emerged as substitutes for the traditional Si-based semiconductor memory devices, which are encountering severe scaling down technical obstacles. In particular, the resistance random access memory (RRAM) and magnetic random access memory (MRAM) hold great promise for the in-memory computing, which are regarded as the optimal strategy and pathway to solve the von Neumann bottleneck by high-throughput in situ data processing. As far as the active materials in RRAM and MRAM are concerned, organic semiconducting materials have shown increasing application perspectives in memory devices due to their rich structural diversity and solution processability. With the introduction of metal elements into the backbone of molecules, some new properties and phenomena will emerge accordingly. Consequently, the RRAM and MRAM devices based on metal-containing organic compounds (including the small molecular metal complexes, metallopolymers, metal-organic frameworks (MOFs) and organic-inorganic-hybrid perovskites (OIHPs)) have been widely explored and attracted intense attention. In this review, we highlight the fundamentals of RRAM and MRAM, as well as the research progress of the applications of metal-containing organic compounds in both RRAM and MRAM. Finally, we discuss the challenges and future directions for the research of organic RRAM and MRAM.

A Robust Memristor Based on Epitaxial Vertically Aligned Nanostructured BaTiO3−CeO2 Films on Silicon

Abstract: With the exploration of ferroelectric materials, researchers have a strong desire to explore the next generation of non‐volatile ferroelectric memory with silicon‐based epitaxy, high‐density storage, and algebraic operations. Herein, a silicon‐based memristor with an epitaxial vertically aligned nanostructures BaTiO3–CeO2 film based on La0.67Sr0.33MnO3/SrTiO3/Si substrate is reported. The ferroelectric polarization reversal is optimized through the continuous exploring of growth temperature, and the epitaxial structure is obtained, thus it improves the resistance characteristic, the multi‐value storage function of five states is achieved, and the robust endurance characteristic can reach 109 cycles. In the synapse plasticity modulated by pulse voltage process, the function of the spiking‐time‐dependent plasticity and paired‐pulse facilitation is simulated successfully. More importantly, the algebraic operations of addition, subtraction, multiplication, and division are realized by using fast speed pulse of the width ≈50 ns. Subsequently, a convolutional neural network is constructed for identifying the CIFAR‐10 dataset, to simulate the performance of the device; the online and offline learning recognition rate reach 90.03% and 92.55%, respectively. Overall, this study paves the way for memristors with silicon‐based epitaxial ferroelectric films to realize multi‐value storage, algebraic operations, and neural computing chip applications.

Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing

Abstract: Two-dimensional (2D) transition metal chalcogenides (TMC) and their heterostructures are appealing as building blocks in a wide range of electronic and optoelectronic devices, particularly futuristic memristive and synaptic devices for brain-inspired neuromorphic computing systems. The distinct properties such as high durability, electrical and optical tunability, clean surface, flexibility, and LEGO-staking capability enable simple fabrication with high integration density, energy-efficient operation, and high scalability. This review provides a thorough examination of high-performance memristors based on 2D TMCs for neuromorphic computing applications, including the promise of 2D TMC materials and heterostructures, as well as the state-of-the-art demonstration of memristive devices. The challenges and future prospects for the development of these emerging materials and devices are also discussed. The purpose of this review is to provide an outlook on the fabrication and characterization of neuromorphic memristors based on 2D TMCs.

Experimental photonic quantum memristor

Abstract: Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures.

Design and Analysis of Multiscroll Memristive Hopfield Neural Network With Adjustable Memductance and Application to Image Encryption

Abstract: Memristor is an ideal electronic device used as an artificial nerve synapse due to its unique memory function. This article presents a design of a new Hopfield neural network (HNN) that can generate multiscroll attractors by utilizing a new memristor as a synapse in the HNN. Differing from the others, this memristor is constructed with hyperbolic tangent functions. Taking the memristor as a self-feedback synapse of a neuron in the HNN, the memristive HNN can yield multidouble-scroll attractors, and its parameters can be used to effectively control the number of double scrolls contained in an attractor. Interestingly, the generation of multidouble-scroll attractors is independent of the memductance function but depends only on the internal state equation. Thus, the memductance function can be adjusted to yield various complex dynamical behaviors. Moreover, amplitude control effects and quantitatively controllable multistability are revealed by numerical analysis. The accurate reproduction of some dynamical behaviors by a designed circuit verifies the correctness of the numerical analysis. Finally, based on the proposed memristive HNN, a novel image encryption scheme in the 3-D setting is designed and evaluated, demonstrating its good encryption performances.