Dynamics Of Memristor Research Articles

Dynamics of memristor coupled discrete fractional symmetric neural network model

Neural networks, also known as artificial neural networks (ANNs), represent innovative computational models inspired by the functioning of biological neurons in the human body. Currently, neural network models are employed to address intricate real-world challenges. This article focuses on constructing and dynamically analyzing interconnected symmetric neural network models inspired by the Hopfield type. These models incorporate discrete fractional memristor elements featuring quadratic memductance. The primary objective is to comprehend system characteristics by introducing nonlinearity to the inter-neuron weights in a functional manner. Additionally, the study explores the influence of electromagnetic radiation on neurons. Bifurcation diagrams, considering fractional order and Lyapunov exponents, illustrate the chaotic nature of the interconnected neural network model. The investigation delves into coexisting system states, examining coexisting bifurcations and attractors. The article offers potential for developing bio-inspired, energy-efficient, and adaptive neural network architectures, thereby contributing to advancements in artificial intelligence and neuromorphic applications.

Analysis and Applications of the Chaotic Hyperbolic Memristor Model with Fractional Order Derivative

This work presents a new five-dimensional fractional-order chaotic system that includes a feedback memristor. A Lorenz-Stenflo-based fractional-order chaotic system with five dimensions that contains feedback memristor dynamics is used to do this. This work focuses on chaotic models through the ABC fractional derivative, a concept we rigorously examine. We designed and usedsophisticated numerical algorithms to model fractional-order dynamics with the utmost precision to understand this system’s complex characteristics. These numerical approaches excel at non-integer order differentiation, which standard numerical methods struggle with. Our research uses fractional calculus to increase the system’s complexity and robustness, revealing its hyperchaotic nature. We demonstrate that the system is chaotic and stable over fractional orders using eigenvalues, Lyapunov exponents, Kaplan-Yorke dimensions, maximal exponents, phase portraits, and equilibrium points. Our conclusions are strengthened by using these numerical systems, intended for this work, and analytical tools. We improve our understanding of fractional-order chaotic systems with our research. It illuminates how to improve electrical integrations and create more sophisticated nonlinear dynamical systems. This work establishes the approach and insights for future research, guiding the development of new systems with enhanced dynamical features

Tunable Neuromorphic Switching Dynamics via Porosity Control in Mesoporous Silica Diffusive Memristors.

In response to the growing need for efficient processing of temporal information, neuromorphic computing systems are placing increased emphasis on the switching dynamics of memristors. While the switching dynamics can be regulated by the properties of input signals, the ability of controlling it via electrolyte properties of a memristor is essential to further enrich the switching states and improve data processing capability. This study presents the synthesis of mesoporous silica (mSiO2) films using a sol-gel process, which enables the creation of films with controllable porosities. These films can serve as electrolyte layers in the diffusive memristors and lead to tunable neuromorphic switching dynamics. The mSiO2 memristors demonstrate short-term plasticity, which is essential for temporal signal processing. As porosity increases, discernible changes in operating currents, facilitation ratios, and relaxation times are observed. The underlying mechanism of such systematic control was investigated and attributed to the modulation of hydrogen-bonded networks within the porous structure of the silica layer, which significantly influences both anodic oxidation and ion migration processes during switching events. The result of this work presents mesoporous silica as a unique platform for precise control of neuromorphic switching dynamics in diffusive memristors.

Hysteresis in memristors produces conduction inductance and conduction capacitance effects.

Memristors are devices in which the conductance state can be alternately switched between a high and a low value by means of a voltage scan. In general, systems involving a chemical inductor mechanism as solar cells, asymmetric nanopores in electrochemical cells, transistors, and solid state memristive devices, exhibit a current increase and decrease over time that generates hysteresis. By performing small signal ac impedance spectroscopy, we show that memristors, or any other system with hysteresis relying on the conductance modulation effect, display intrinsic dynamic inductor-like and capacitance-like behaviours in specific input voltage ranges. Both the conduction inductance and the conduction capacitance originate in the same delayed conduction process linked to the memristor dynamics and not in electromagnetic or polarization effects. A simple memristor model reproduces the main features of the transition from capacitive to inductive impedance spectroscopy spectra, which causes a nonzero crossing of current-voltage curves.

A memphotoristor for high-efficiency temporal vision processing

A photodetector with history-dependent dynamical responses to optical inputs could offer an essential breakthrough in performing temporal vision processing and time-series prediction without the requirement for sophisticated circuitry and undesired high energy consumption. Till now, memristor dynamics and its effective modulation with photo illumination have been utilized to mimic bio-inspired vision processing. Despite significant effort, it is still challenging to process real-time analogue temporal optical information with high accuracy, which requires developing a photonic counterpart of the electric-triggered memristor. Here, we report on the development of a Ga2O3-based proof-of-concept memphotoristor, in which memristive dynamics were stimulated with ultraviolet photon flux rather than conventional voltage. Specifically, a distinct hysteresis loop opening appeared in the cyclic photocurrent-ultraviolet intensity curves, where the magnitude of the loop opening depends on the photon flux. Additionally, light-illumination-induced fading memory, nonlinearity, and temporal dynamics were successfully utilized to demonstrate in-sensor reservoir computation and time-series prediction with an accuracy of 98.86%. Our research will pave the way to developing a wide range of cutting-edge optoelectronics for various applications, such as photosensors, photonic memory storage, and neuromorphic vision sensing of objects in real time.

Shortcuts to Edge-of-Chaos Domains of Memristive Circuits and Historic Measurement of Contiguous Triple-Branch V – I Curve of Chua Corsage Memristor

Locally-active memristors blessed with an edge-of-chaos domain, which can be destabilized for generating action potentials, are natural candidates for emulating biological neurons. Pinpointing the edge-of-chaos domain, where neuromorphic behaviors may occur, is important for studying neuromorphic dynamics of memristors. This paper proposes a short-cut method for locating the edge-of-chaos domains in two kinds of generic memristors, and in several typical memristive 1-port circuits using only the Jacobian matrix in terms of their equations. Taking the Chua Corsage Memristor (CCM) and several CCM-based memristive 1-port circuits as examples, we verify the proposed new methods, and calculates their edge-of-chaos domains. Also, we carry out a complete classification of all the parameter regions of the CCM and several CCM-based memristive 1-port circuits, namely the locally-passive, locally-active but unstable, and edge-of-chaos domains, under both voltage and frequency control. Near the calculated edge-of-chaos domain, we uncover some new neuromorphic behaviors. To confirm the physical interpretations and predictions of the edge-of-chaos theorem, this paper presents an inexpensive electronic circuit realization of the complete set of equations defining the CCM using only off-the-shelf circuit components. When this poor-man’s memristor is connected to various linear passive R, L, C circuits, and a battery, the resulting circuit can be tuned to generate action potentials, and a garden variety of neuromorphic phenomena, including chaos. But the highlight of this paper is reserved for the CCM itself where the world’s first oscilloscope picture of a contiguous self-intersecting triple-branch DC V–I curve of the CCM is displayed in real time.

Edge of Chaos Kernel and neuromorphic dynamics of a locally-active memristor

Edge of Chaos Kernel (EOCK), the crown jewel of edge of chaos, is a nature’s optimal mechanism for creating action potentials, which can be exploited to uncover the neuromorphic dynamics of memristors. A memristor blessed with an EOCK, which can emulate the neuromorphic behaviors of biological neurons, is a natural candidate to be applied in neuromorphic computing. This paper proves that a first-order memristor blessed with an EOCK has a pole in the open left half plane (LHP), and presents a simple method for testing if a generic voltage-controlled first-order memristor has an EOCK using only its first-order Taylor series expansion coefficients. This paper presents, for the first time, that locally active voltage-controlled generic memristors contain an EOCK. Also, this paper designs a theoretical memristor model blessed with an EOCK, and constructs a memristive circuit for observing the relationship between the EOCK and neuromorphic behaviors. The memristor blessed with an EOCK generates some well-known neuromorphic behaviors, further verify the innate ability of Edge of Chaos Kernel to exhibit the complex phenomena. This paper pave the way towards further expounding the generating mechanism of the action potential from the perspective of circuit.

Role of defects in resistive switching dynamics of memristors

Role of defects in resistive switching dynamics of memristors

Tri-valued memristor-based hyper-chaotic system with hidden and coexistent attractors

Recently, the nonlinear dynamics of memristor has attracted much attention. In this paper, a novel four-dimensional hyper-chaotic system (4D-HCS) based on a tri-valued memristor is found. Theoretical analysis shows that the 4D-HCS has complex hyper-chaotic dynamics such as hidden attractors and coexistent attractors. We also experimentally analyze the dynamics behaviors of the 4D-HCS in aspects of the phase diagram, bifurcation diagram, Lyapunov exponential spectrum, power spectrum and the correlation coefficient. To rigorously verify the chaotic behavior, we analyze the topological horseshoe of the system and calculate the topological entropy. In addition, the comparison with binary-valued memristor-based chaotic system shows that a chaotic system with a tri-valued memristor can generate hyper-chaos and coexistent attractors, while the one with a binary-valued memristor cannot. This finding suggests that applying three- or multi-value memristors in chaotic circuits can produce more complex dynamic properties than binary-valued memristors. To show the easy implementation of the 4D-HCS, we implement the 4D-HCS in an analogue circuit-based hardware platform, and the implementation results are consistent with the theoretical analysis. Finally, using the 4D-HCS, we design a pseudorandom number generator to explore its potential application in cryptography.

Toward Simplified Physics-Based Memristor Modeling of Valence Change Mechanism Devices

Memristors are promising nanoelectronic devices for the implementation of future AI-driven sensor-processor electronic systems, which are essential for the ongoing digitalization of our world. Accurate and computationally cost-effective models for the manufactured memristors are essential for the design of such systems, especially for the simulation of large circuits. In this brief we address the simplification of the JART memristor model, a generic physics-based model of Valence Change Mechanism (VCM) memristors which accurately describes the dynamic behavior of fabricated memristor devices. Furthermore, the proposed model and simplification methodology have the potential to capture the dynamics of a wide range of memristor devices. Importantly, the implicit description of the current through the memristor is replaced by an explicit mathematical relationship. The proper reproduction of memristor dynamics, verified by applying the system-theoretic Dynamic Route Map (DRM) graphical analysis tool, applicable to first-order systems, can be observed through the proposed simplified model and enables the time-efficient simulation of large arrays of VCM devices.

Polariton-Based Quantum Memristors

Information processing and storage by the same physical system is emerging as a promising alternative to traditional computing platforms. In turn, this requires the realization of elementary units the memory content of which can be easily tuned and controlled. Here, we introduce a polariton-based quantum memristor where the memristive nature arises from the intercavity polariton exchange and is controlled by a time-varying atom-cavity detuning. A dynamical hysteresis is characterized by the fluctuations in the instantaneous polariton number, where the history information is encoded into a dynamical phase. Using a Lindblad master-equation approach, we find that features of the quantum memristor dynamics, such as the area and circulation of the hysteresis loop, showcase a kind of ``plasticity'' controlled by quantum state initialization. This makes this quantum memristor very versatile for a wide range of applications.

Symmetry and asymmetry induced dynamics in a memristive twin-T circuit

ABSTRACT The dynamics of memristor–based chaotic oscillators with perfect symmetry is very well documented. However, the literature is relatively poor concerning the behaviour of such types of circuits when their symmetry is perturbed. In this paper, we consider the dynamics of a memristive twin-T oscillator . Here, the symmetry is broken by assuming a memristor with an asymmetric pinched hysteresis loop characteristics. A variable disturbance term is introduced into the current-voltage relationship of the memristor in order to obtain an asymmetric characteristic. Phase portraits, bifurcations, basins of attraction, and Lyapunov exponents are used to illustrate various nonlinear patterns experienced by the underlined memristive circuit. It is shown that in the absence of the disturbance term, the characteristic of the memristor is perfectly symmetric which induces typical behaviours such as coexisting symmetric bifurcation and bubbles, spontaneous symmetry-breaking, symmetry recovering, and coexistence of several pairs of mutually symmetric attractors. With the perturbation term, the symmetry of the oscillator is destroyed resulting in more complex nonlinear phenomena such as coexisting asymmetric bubbles of bifurcation, critical transitions, and multiple coexisting (i.e. up to five) asymmetric attractors. Also, PSpice simulation studies confirm well the results of theoretical predictions.

Memristor dynamics involved in cells communication for a 2D non‐linear network

In this study, the authors consider a first step to apply memristor devices in a cellular non-linear network (CNN), where the advantages of the non-linearity, nanoscalability and memory effect could be taken into account to implement a 2D-CNN for signal and image processing – Memristors are used in the coupling mode to connect adjacent cells serially. They drive the analytical model describing the transmission of information from one cell to another via memristor, whose description in the ϕ –q plane is given, then improved to overcome the problem of discontinuities for some values of the charge q ( t ) . The modified model is then chosen to be continued for all initial conditions and all parameters sets. Moreover, numerical simulations from SPICE and MATLAB software confirm the authors’ analytical predictions.

Ferroic tunnel junctions and their application in neuromorphic networks

Brain-inspired neuromorphic computing has been intensively studied due to its potential to address the inherent energy and throughput limitations of conventional Von-Neumann based computing architecture. Memristors are ideal building blocks for artificial synapses, which are the fundamental components of neuromorphic computing. In recent years, the emerging ferroic (ferroelectric and ferromagnetic) tunnel junctions have been shown to be able to function as memristors, which are potential candidates to emulate artificial synapses for neuromorphic computing. Here, we provide a review on the ferroic tunnel junctions and their applications as artificial synapses in neuromorphic networks. We focus on the development history of ferroic tunnel junctions, their physical conduction mechanisms, and the intrinsic dynamics of memristors. Their current applications in neuromorphic networks will also be discussed. Finally, a conclusion and future outlooks on the development of ferroic tunnel junctions will be given. Our goal is to give a broad review of ferroic tunnel junction based artificial synapses that can be applied to neuromorphic computing and to help further ongoing research in this field.

Memristor design rules for dynamic learning and edge processing applications

The ability to dynamically learn and adapt to changes in the environment is one of the hallmarks of biological systems. In this work, we identify the subset of the design space of memristive materials that is optimal for dynamic learning applications. Using an architecture inspired on the learning center of the insect brain, we implement a model system consisting of a discrete implementation of spiking neurons where dynamic learning takes place on a set of plastic synapses formed by memristor pairs in a crossbar array. Using two separate benchmarks, one comprising the dynamic learning of the Modified National Institute of Standards and Technology dataset and another one targeting one shot learning, we have identified the key properties that memristive materials should have to be optimal dynamic learners. The results obtained show that a fine degree of control of the memristor internal state is key to achieve high classification accuracy during dynamic learning but that within this optimal region learning is extremely robust both to device variability and to errors in the writing of the internal state, in all cases allowing for 2σ variations greater than 40% without significant loss of accuracy, hence overcoming two of the perceived limitations of memristors. By additionally requiring that learning takes place concurrently to information processing, we are able to derive a set constraints to the memristor dynamics.

Memristive Devices and Networks for Brain‐Inspired Computing

In the Review@RRL by Yuchao Yang, Ru Huang and co-workers (article no. 1900029), existing approaches for the implementation of artificial synapses based on metal ion transport, oxygen ion transport and phase change mechanism, etc. are overviewed, and their advantages and disadvantages are analyzed. This is followed by latest progresses on memristive neurons, ranging from leaky-integrate-and-fire neuron to Hodgkin-Huxley neuron. From a system perspective, both neural network accelerators that exploit the in-memory and analog characteristics of memristive arrays as well as neuromorphic systems based on the intrinsic dynamics of memristors are discussed. The authors finally point out the outstanding challenges this field faces and highlight the importance of co-optimization between devices, circuits and algorithms.

Effects of Parasitic Fractional Elements to the Dynamics of Memristor

In this research, we study the effects of the parasitic fractional elements to the dynamic of the memristor where both flux- and charge-controlled memristors have been considered. For doing so, the fractional differential equation-based approach has been used for modeling the memristor and the memristor-based circuits under the effects of the parasitic fractional elements where the resulting equations have been solved both analytically and numerically. From the obtained solutions and simulations, the effects of the parasitic fractional elements to the dynamic of the memristor have been studied. We have found that the parasitic fractional elements cause the charge and flux decay of the memristor similarly to the conventional parasitic elements. Moreover, the impasse points of the phase portraits between flux and charge of the memristor-based circuits can also be broken by the parasitic fractional elements. The effects of the order and the nonlinearity of the parasitic fractional elements have also been studied.

Memristive Devices and Networks for Brain‐Inspired Computing

As the era of big data approaches, conventional digital computers face increasing difficulties in performance and power efficiency due to their von Neumann architecture. As a result, there is recently a tremendous upsurge of investigations on brain‐inspired neuromorphic hardware with high parallelism and improved efficiency. Memristors are considered as promising building blocks for the realization of artificial synapses and neurons and can therefore be utilized to construct hardware neural networks. Here, a review is provided on existing approaches for the implementation of artificial synapses and neurons based on memristive devices; and the respective advantages and disadvantages of these approaches are evaluated. This is followed by a discussion of hardware accelerators and neuromorphic computing systems that exploit the parallel, in‐memory and analog characteristics of memristive crossbar arrays as well as the intrinsic dynamics of memristors. Finally, the outstanding challenges are addressed that have not yet been resolved in the present studies, and future advances are discussed that might be needed for building intelligent and energy efficient neuromorphic systems.

Tunnel current model of asymmetric MIM structure levying various image forces to analyze the characteristics of filamentary memristor

Electro-formation in metal insulator metal (MIM) structure causes the emergence of the conductive filament and also leaves a tiny insulating gap amid upper electrode and conductive filament. Since the material property of conductive filament is unlike that of the upper electrode, it is vital to consider these dissimilarities of electrodes in tunneling phenomena. In this paper, the trapezoidal potential barrier of an insulating film sandwiched between two dissimilar electrodes (asymmetric MIM) is accounted for and superimposed with triangular, parabolic and rectangular image force potentials, to derive the corresponding mean barrier heights. Then, the impact of the insulating film thickness, metal’s work function difference, and the dielectric constant of the insulating film on tunneling current density are investigated, employing the same mean barrier potential. Finally, the obtained results are implemented in conductive filament-based memristor model to analyze its physical behavior through pinched hysteresis I–V characteristics. It is ascertained that symmetric results are obtained in forward and reverse biased conditions. The incorporation of triangular image potential suppresses the barrier foremost, whereas parabolic image potential (PIP) has moderate effects on barrier lowering and rectangular image potential exhibits least lowering of the potential barrier, which renders minimum current. The switching dynamics of memristor apprises that PIP manifests only 0.5% deviation with experimental results in OFF switching and also shows good agreement in ON switching. The design is endorsed by comparing the outcomes with experimental and simulated data. This model enables reconfigurable and neuromorphic computing applications of the memristor.

Dynamical attractors of memristors and their networks

It is shown that the time-averaged dynamics of memristors and their networks periodically driven by alternating-polarity pulses may converge to fixed-point attractors. Starting with a general memristive system model, we derive basic equations describing the fixed-point attractors and investigate attractors in the dynamics of ideal, threshold-type and second-order memristors, and memristive networks. A memristor potential function is introduced, and it is shown that in some cases the attractor identification problem can be mapped to the problem of potential function minimization. Importantly, the fixed-point attractors may only exist if the function describing the internal state dynamics depends on an internal state variable. Our findings may be used to tune the states of analog memristors, and also be relevant to memristive synapses subjected to forward- and back-propagating spikes.