Memristor Model Research Articles

Learning Model Based on Electrochemical Metallization Memristor with Cluster Residual Effect

Although a memristor model, subjected to electrochemical metallization mechanism, has been proposed based on the spontaneous decay of clusters in the previous work, it does not agree with the human forgetting accurately. Therefore, an improved model is meaningfully presented for the memristor with the cluster spontaneous decay by adding the residual effect. The former is due to the inward contraction of atoms driven by surface energy, while the latter is because of the balance of attractive and repulsive forces between atoms. The model fits well with the actual device. The forgetting is caused by the spontaneous decay. Memory retention is generated due to the added effect, which is also the internal cause of good agreement with the actual forgetting. Additionally, short‐term plasticity is converted to long‐term plasticity through the repeated learning. The efficiency of experiential learning using this model is much higher than that using the previous. It is shown that the physical mechanism of spontaneous decay in the cluster‐based channel is different from that in vacancy‐based or atom‐based channel. The model working under a non‐ideal condition with the temperature influence is discussed. Potential applications based on the model are stated.

Dynamical analysis of a discrete Aihara neuron under a locally active memristor as electromagnetic radiation and its DSP implementation

The study of neuron model under electromagnetic radiation is significant for learning brain principles and treating mental diseases. In this paper, a novel discrete locally active memristor(DLAM) model is designed and its characteristics are investigated thoroughly. Then, the DLAM is used to imitate electromagnetic radiation to stimulate Aihara neuron, called EMR-Aihara neuron model. The equilibrium point of this discrete model is analyzed. Dynamical characteristics are studied by means of phase diagram, iteration sequence, bifurcation diagram, Lyapunov Exponent spectrum(LEs), Kolmogorow entropy(KE) and Spectral Entropy(SE) complexity. With these analysis methods, rich dynamical behaviors and neuron firing patterns are discovered from the EMR-Aihara neuron map, including hyperchaos, chaos and period. In addition, complex multistability and state transition phenomena concerning various attractors and neuron firing modes are observed. This EMR-Aihara neuron map is implemented in digital circuit by DSP platform as well, confirming the physical availability of the model. The EMR-Aihara neuron model can simulate biological neuron under electromagnetic radiation and apply to image encryption.

A composite model of memristors based on barrier and dopant drift mechanisms

This paper presents a hybrid model for TiO2-based memristors, integrating the dopant drift mechanism with the Schottky barrier theory. We introduce the movement of oxygen vacancies as a dynamic variable to modulate changes in memristors. Furthermore, the variation of the dominate mechanism of the TiO2 memristors under different operating conditions is studied, which is related to the position of the internal oxygen vacancy. The proposed model accurately captures the rectification linearity, and effectively elucidates the dominant current mechanisms manifested in six distinct regions of the I-V curves. Our model exhibits better predication with reduced errors when applied to Pt/TiO2/Pt memristors. The proposed model can well describe the dual-mechanism memristor phenomenon, and provides a reference for the subsequent study of multi-mechanism behavior in memristors.

Cross-Channel Color Image Encryption Scheme Based on Discrete Memristive Coupled Neurons and DWT Compression

To address the consumption and security of color images for transmission and storage, a cross-channel color image encryption scheme based on a discrete memristive coupled neuron model and DWT compression is designed in this article. Firstly, the dynamics of the discrete memristive coupled neuron system are analyzed and found to possess the hyperchaotic phenomenon, which provides sufficient security for the encryption scheme. Secondly, the color image processed by discrete wavelet transform (DWT) has a quarter of the previous capacity. Then, the color image is combined with a Hash function, and the resulting Hash sequence is given the initial value of the hyperchaotic system. Next, a particle swarm foraging algorithm (PSFA) is designed to better disrupt the correlation in the RGB channel. Finally, a complementary DNA coding rule is implemented for the further encryption of color images. Simulation results show that even with DWT lossy compression, the recovered image can be clearly seen. The performance analysis illustrates that under the hyperchaotic system, the proposed encryption algorithm brings higher security for color images.

Extreme multi-stability and microchaos of fractional-order memristive Rulkov neuron model considering magnetic induction and its digital watermarking application

Extreme multi-stability and microchaos of fractional-order memristive Rulkov neuron model considering magnetic induction and its digital watermarking application

Dynamics and Implementation of FPGA for Memristor-Coupled Fractional-Order Hopfield Neural Networks

The coupling between neurons can lead to diverse neural network architectures, with the Hopfield neural network (HNN) being particularly noteworthy for its resemblance to human brain function and its potential in modeling chaotic systems. This paper introduces a novel approach: a fractional-order HNN coupled with a hyperbolic tangent-type memristor. Initially, we propose a new model for the hyperbolic tangent-type memristor and fingerprints. Subsequently, we construct a memristor-coupled fractional-order Hopfield neural network (mFOHNN) and explore its dynamic behavior using various analytical tools, including phase diagrams, bifurcation diagrams, Lyapunov exponent diagrams, Poincaré maps, and attractor basins. Our findings reveal rich coexisting bifurcation behavior in the neural network model, influenced by different initial values of coexisting attractors. Finally, we validate the model through analysis and implementation using Multisim circuit simulation software and FPGA hardware, respectively.

Constructing Memristive Hindmarsh–Rose Neuron with Countless Coexisting Firings

Memristor synapses have been widely introduced into neuronal models to investigate the effects of external magnetic fields. However, there is a relative lack of research on the external-induced electric fields in neurons. In this paper, a 4D-memristive Hindmarsh–Rose neuron model is constructed by introducing a memristor and an electric field variable, which can generate complex neural firing. Notably, numerical simulations reveal that the initial conditions of the memristor can induce different firing patterns, exhibiting a unique fractal structure in the basin of attractions. Remarkably, the offset parameters of the internal variables of the neuron can be canceled out so that the offset boosting of the variables can be achieved according to the initial values, giving rise to an uncountably many hidden attractors with homogeneous multistability. This model provides the first example of generating uncountably many attractors in a memristive neuron model without relying on trigonometric functions, significantly advancing our understanding of neuronal dynamics. Finally, a digital circuit is designed and implemented on the RISC-V platform to verify the numerical simulation and theoretical analysis. The findings of this study have a certain implication for the development of advanced neuromorphic computing systems and the understanding of complex neuronal behaviors in the presence of external electric fields.

Design and analysis of image encryption based on memristor chaotic systems with hidden attractors

Chaotic systems with hidden attractors are important research hotspots in nonlinear science. In recent years, continuous chaotic systems with special equilibrium points have been widely studied, especially continuous chaotic systems without equilibrium points. A four-dimensional chaotic system devoid of equilibrium point is proposed by using a composite cosine piecewise-linear (COS-PWL)function and a memristor model based on the modified Sprott-C system. The dynamical behaviors of the new system are studied through numerical analysis methods. The results reveal it is extremely sensitive to control parameters as well as initial conditions, which make it particularly suitable for application to image encryption algorithms. In light of these findings, a novel image encryption algorithm is introduced. This algorithm integrates the new four-dimensional chaotic system with ribonucleic acid (RNA) coding technology. Furthermore, the simulation results and security analyses validate the rationality of the encryption mechanism and the robustness of the encryption security of the proposed image encryption algorithm.

Exploiting 2D-SDMCHM and matching embedding driven by flag-shaped hexagon prediction for visually meaningful medical image cryptosystem

With the proliferation of advanced medical technologies and smart wearable devices, a considerable volume of patient data and medical images is being generated and stored, leading to an escalating concern over patient privacy violations and manipulation. Therefore, safeguarding the secrecy and integrity of medical images is paramount for preserving patient privacy and ensuring accurate medical diagnostics. To tackle this challenge, this paper introduces a novel approach, the Visually Meaningful Medical Image Cryptosystem (VMMIC), leveraging a 2-Dimensional Sinusoidal Discrete Memristor Coupled Hyperchaotic Map (2D-SDMCHM) to protect the privacy of medical images. The innovation of this study is primarily reflected in three aspects: Firstly, a novel Discrete Memristor (DM) model is proposed, upon which a new 2D-SDMCHM is constructed. Comparative analysis showcases its superior chaos robustness, as evidenced by evaluations through bifurcation diagrams, phase trajectories, Lyapunov exponents, and Shannon entropy. Secondly, we introduce a measurement matrix optimization algorithm based on Optimal Matrix Arrangement (OMA) for Compressive Sensing (CS), aimed at minimizing the coherence of the measurement matrix, thereby enhancing reconstruction accuracy. Lastly, a Matching Embedding Method Driven by Flag-Shaped Hexagon Prediction (MEMFHP) is introduced to ensure lossless concealment of confidential information. Experimental findings and comparative evaluations affirm the VMMIC's capability to meet high standards of reconstruction quality, steganography, and efficiency in handling medical images, while also demonstrating robustness against various attacks. Consequently, the proposed VMMIC holds significant promise for practical implementation in engineering applications.

Dynamics modeling of a memristor-based Rucklidge chaotic system: Multistability, offset boosting control and FPGA implementation

The complex dynamics greatly contributes to the application of chaos, especially the memristor chaotic systems that have emerged very recently. In this paper, a novel four-dimensional continuous chaotic system is developed by introducing a generalized memristor model into the classical Rucklidge system, and the rich dynamical traits are confirmed by theories and numerical simulations. System Hamilton energy is deduced and analyzed to expose the underlying energy evolution and chaos mechanism. And then, realizing the offset boosting control of the system promotes its engineering applications. Numerical simulations verify that the offset boosting controller can directly change the polarity of the chaotic signal. Specifically, bifurcation diagram, Lyapunov spectra, system complexity evolution diagram and dynamic evolution map are adopted to expound the rich dynamics. Meanwhile, the extreme multi-stability with various initial values resulting in an infinite number of coexisting attractors is also revealed. Finally, the proposed attractor is implemented using the Field Programmable Gate Array (FPGA) platform and building analog circuit based on Multisim. Among which, the former is potentially used to design the Pseudo-random signal generator in information encryption, while the latter is for verifying the existence of the proposed chaotic system.

Cap like trajectories in 5D chaotic tangent hyperbolic memristive model: fractional calculus and encryption

This research aims to investigate the influence of model parameters and fractional order on a novel mathematical model with tangent hyperbolic memristor. This investigation conducted by applying Lyapunov exponents and bifurcation analysis. We utilize the Lyapunov exponent theory to understand and characterize these chaotic behaviors under fractional indices. The Lyapunov exponent, bifurcation, and phase diagrams have been depicted to explore the intricate dynamics of the chaotic system governed by the chaotic equation. A novel approach termed Atangana-Baleanu-Caputo (ABC) fractional derivative (FD) to generate phase portraits and gain insights into the system’s behavior. The random numbers generated by the chaotic system are employed to distort the image through an amalgamated image encryption (AIE) algorithm. Subsequently, the integrity of the scrambled image has been assessed using various image security evaluation methods to reinforce the notion that combining the chaotic system and image can constitute a valuable encryption key. Finally, the chaotic model circuit realization uses active and passive components, and the outcomes are compared with the numerical simulations.

Modified nonlinear ion drift model for TiO2 memristor: a temperature dependent study

The creation and optimisation of memristor models with different topologies and physical mechanisms have received increasing attention in recent years. Memristors, known for their unique resistive switching mechanism, have garnered significant interest as promising components for next-generation computing. However, to effectively design and test memristor-based circuits, it is crucial to have a mathematical representation of the experimentally determined current-voltage characteristics of memristors. This paper proposes a model and conducts an analysis that offers insights into memristor technology, beginning with its characteristics and extending to simulations involving various parameters. The proposed model and its dependency on temperature are implemented using MATLAB. The model captures changes in current characteristics concerning the fundamental voltage without using any window functions. Thus, it accurately represents the variation in memristance with temperature, contributing to a more precise and observed modelling approach.

High-performance convolutional neural network emulation via FPGA-integrated memristive circuitry

Although memristors address the potential data flow and memory access bottlenecks in traditional von Neumann architectures by moving computational operations into the storage elements, thereby improving computational efficiency and reducing energy consumption, the current costs of memristors are relatively high, and they face issues such as low reliability and short lifespan. Therefore, this paper proposes a simulation circuit model based on FPGA, which can provide a low-cost solution for rapid prototyping design and exploration of the design space. The memristor simulation circuit is deployed on the Xilinx ZYNO-7000 FPGA XC7Z020 with a resource utilization rate of less than 1%, and it has been successfully integrated and verified. Furthermore, this paper also constructs a convolutional neural network for MNIST image recognition using a memristor model. With a basis of 512 parameters, it achieves an accuracy of 90.22%.

A Study on Unified Modelling Approach for Memristor: Next Generation Semiconductor Devices

This research investigates the voltage-current (V-I) characteristics of three distinct memristor models, each representing a unique composition and behaviour. The model, denoted as TiN-TiOx-HfOx-Pt-Bilayered Memristor model, is a compact representation designed for Metal–Oxide Resistive Random Access Memory (RRAM). Matlab tool is used for the simulation to analyse V-I characteristics of Memristor. Through comprehensive analysis and simulations, we aim to provide a detailed insight into the intrinsic behaviours of the memristor models. Understanding the V-I characteristics of these models is crucial for their potential applications in emerging memory technologies. This Study elaborate the mathematical modelling of memristor with the outcome untuned V-I Characteristics - DC Switching characteristics. The findings contribute to the broader field of memristor research, fostering advancements in electronic memory devices and computational systems

Synergistic m_GDI-Based ALU Design Using CMOS and VTEAM Memristor Model for Low-Power High-Speed Applications

Synergistic m_GDI-Based ALU Design Using CMOS and VTEAM Memristor Model for Low-Power High-Speed Applications

Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach.

This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.

Dynamic Analysis, Image Encryption Application and FPGA Implementation of a Discrete Memristor-Coupled Neural Network

This paper presents a novel discrete memristor model that incorporates exponential and absolute value functions. A discrete coupled memristor neural network model is constructed based on this memristor design. The periodic and chaotic regions of the discrete neural network model are determined using bifurcation and Lyapunov exponent spectrum. Furthermore, by varying the initial values of the discrete memristive neural network, we observe the coexistence of chaos and periodic attractors, as well as periodic attractors. Additionally, an application to color image encryption based on the discrete system model is given. Security analysis is conducted in the aspects of key space, histogram analysis, correlation analysis, sensitivity analysis, Peak Signal-to-Noise Ratio (PSNR), and information entropy analysis. The analysis results show that the algorithm has a key space size of [Formula: see text], and the information entropy of baboon graph is 7.9993, which is very close to the ideal value of 8. It shows that the image encryption algorithm is feasible and effective. Finally, the implementation of the discrete memristive neural network model is realized using Field Programmable Gate Array (FPGA). The experimental implementation is conducted using the Verilog language on the Vivado 2018.3 platform, and the obtained results align with the numerical simulation results obtained through MATLAB.

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

A memristor crossbar based on a novel ternary memristor model

A memristor crossbar based on a novel ternary memristor model

Energy and self-adaption in a memristive map neuron

Both capacitor and charge-controlled memristor (CCM) can regulate the inner energy levels during the release and accumulation of charges in these electronic components. Combination of inductive and capacitive components is crucial to build an oscillatory circuit, and incorporation of a nonlinear resistor is helpful to support nonlinear oscillation under continuous energy exchange between magnetic field and electric field. Relation between physical variables in neural circuits can be described by some equivalent nonlinear oscillators by applying scale transformation on the variables and parameters for the neural circuit, and the energy function becomes available from theoretical approach. In this work, a simple neural circuit coupled with a CCM is designed to obtain a memristive neuron model, and then linear transformation is applied to the sampled variables involved with time scale for time step, a new memristive map is obtained to discuss its energy characteristic and self-controllability under energy flow. The result provides guidance to measure the reliability of maps, and the new derived maps with reliable physical evidences can be further used in digital signal processing.