Memristive feature and mechanism induced by laser-doping in defect-free 2D semiconductor materials

Memristors as non-volatile memory devices have gained numerous attentions owing to their advantages in storage,in-memory computing, synaptic applications, etc. In recent years, two-dimensional(2D) materials with moderate defects have been discovered to exist memristive feature. However, it is very difficult to obtain moderate defect degree in 2D materials, and studied on modulation means and mechanism becomes urgent and essential. In this work, we realized memristive feature with a bipolar switching and a configurable on/off ratio in a two-terminal MoS_(2) device(on/off ratio ~100), for the first time, from absent to present using laser-modulation to few-layer defect-free MoS_(2)(about 10 layers), and its retention time in both high resistance state and low resistance state can reach 2×10^(4) s. The mechanism of the laser-induced memristive feature has been cleared by dynamic Monte Carlo simulations and first-principles calculations. Furthermore, we verified the universality of the laser-modulation by investigating other 2D materials of TMDs. Our work will open a route to modulate and optimize the performance of 2D semiconductor memristive devices.

Bipolar-growth multi-wing attractors and diverse coexisting attractors in a new memristive chaotic system

This article proposes a non-ideal flux-controlled memristor with a bisymmetric sawtooth piecewise function, and a new multi-wing memristive chaotic system(MMCS) based on the memristor is generated. Compared with other existing MMCSs, the most eye-catching point of the proposed MMCS is that the amplitude of the wing will enlarge towards the poles as the number of wings increases. Diverse coexisting attractors are numerically found in the MMCS, including chaos,quasi-period, and stable point. The circuits of the proposed memristor and MMCS are designed and the obtained results demonstrate their validity and reliability.

SPICE modeling of flux-controlled unipolar memristive devices

Unipolar memristive devices are an important kind of resistive switching devices. However, few circuit models of them have been proposed. In this paper, we propose the SPICE modeling of flux-controlled unipolar memristive devices based on the memristance versus state map. Using our model, the flux thresholds, ON and OFF resistance, and compliance current can easily be set as model parameters. We simulate the model in HSPICE using model parameters abstracted from real devices, and the simulation results show that the proposed model caters to the real device data very well, thus demonstrating that the model is correct. Using the same modeling methodology, the SPICE model of charge-controlled unipolar memristive devices could also be developed. The proposed model could be used to model resistive memory cells, logical gates as well as synapses in artificial neural networks.

A novel color image encryption algorithm based on genetic recombination and the four-dimensional memristive hyperchaotic system

Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.

Memristive network-based genetic algorithm and its application to image edge detection

This paper proposes a mem-computing model of memristive network-based genetic algorithm(MNGA)by building up the relationship between the memristive network(MN)and the genetic algorithm(GA),and a new edge detection algorithm where image pixels are defined as individuals of population.First,the computing model of MNGA is designed to perform mem-computing,which brings new possibility of the hardware implementation of GA.Secondly,MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed.Finally,simulation results demonstrate that the figure of merit(FoM)of our model is better than the latest memristor-based swarm intelligence.In summary,a new way is found to build proper matching of memristor to GA and aid image edge detection.

Global Synchronization of Stochastically Disturbed Memristive Neurodynamics via Discontinuous Control Laws

This paper presents the theoretical results on the master-slave(or driving-response) synchronization of two memristive neural networks in the presence of additive noise. First,a control law with a linear time-delay feedback term and a discontinuous feedback term is introduced. By utilizing the stability theory of stochastic differential equations, sufficient conditions are derived for ascertaining global synchronization in mean square using this control law. Second, an adaptive control law consisting of a linear feedback term and a discontinuous feedback term is designed to achieve global synchronization in mean square, and it does not need prior information of network parameters or random disturbances. Finally, simulation results are presented to substantiate the theoretical results.

A spintronic memristive circuit on the optimized RBF-MLP neural network

A radial basis function network(RBF)has excellent generalization ability and approximation accuracy when its parameters are set appropriately.However,when relying only on traditional methods,it is difficult to obtain optimal network parameters and construct a stable model as well.In view of this,a novel radial basis neural network(RBF-MLP)is proposed in this article.By connecting two networks to work cooperatively,the RBF’s parameters can be adjusted adaptively by the structure of the multi-layer perceptron(MLP)to realize the effect of the backpropagation updating error.Furthermore,a genetic algorithm is used to optimize the network’s hidden layer to confirm the optimal neurons(basis function)number automatically.In addition,a memristive circuit model is proposed to realize the neural network’s operation based on the characteristics of spin memristors.It is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33%accuracy in the processing of the Modified National Institute of Standards and Technology(MNIST)dataset classification task.The experimental results show that the method has considerable application value.

Low-power emerging memristive designs towards secure hardware systems for applications in internet of things

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are presented.By reviewing the state-of-the-art in three highlighted memristive application areas,i.e.memristive non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,respectively.For the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security.This review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.

Heterogeneous dual memristive circuit:Multistability,symmetry,and FPGA implementation

An improved heterogeneous dual memristive circuit(DMC)is proposed based on Chua's circuit,which shows good symmetry and multistablility.For the difficulty in controlling the initial conditions,which restricts the engineering applications,the 3 rd-order model(3 OM)in flux-charge domain is derived from the 5 th-order model(5 OM)in volt-ampere domain by using the flux-charge analysis method(FCAM).The consistence of symmetry and multistability before and after dimensionality decreasing is meticulously investigated via bifurcation diagram,Lyapunov exponents,and especially attraction basins.The comparative analysis validates the effectiveness of reduction model and improves the controllability of the circuit.To avoid the noise in the analog circuit,a field-programmable gate array(FPGA)is utilized to realize the reduction model,which is rarely reported and valuable for relevant research and application.

Memristive Behavior Based on Ba-Doped SrTiO3 Films

The Sr0.95Ba0.05 TiO3 （SBT） nanometer film is prepared on the commercially available Pt/TiO2/SiO2/Si substrate by radio-frequency magnetron sputtering. The x-ray diffraction pattern and the scanning electron microscope image of the cross-sectional profile of the SBT nanometer film are depicted. The memristive mechanism is inferred. The mathematical model M（q） = 12.3656 - 267.4038｜q（t）｜is calculated, where M（q） denotes the memristance depending on the quantity of electric charge, and q（t） denotes the quantity of electric charge depending on the time. The theoretical I-V characteristics of the SBT nanometer film are obtained by the mathematical model. The results show that the theoretical I-V characteristics are consistent with the measured I-V characteristics. Moreover, the mathematical model could guide the research on applications of the memristor.

Hidden Attractors in a Delayed Memristive Differential System with Fractional Order and Chaos Synchronization

As an important research branch,memristor has attracted a range of scholars to study the property of memristive chaotic systems.Additionally,time⁃delayed systems are considered a significant and newly⁃developing field in modern research.By combining memristor and time⁃delay,a delayed memristive differential system with fractional order is proposed in this paper,which can generate hidden attractors.First,we discussed the dynamics of the proposed system where the parameter was set as the bifurcation parameter,and showed that with the increase of the parameter,the system generated rich chaotic phenomena such as bifurcation,chaos,and hypherchaos.Then we derived adequate and appropriate stability criteria to guarantee the system to achieve synchronization.Lastly,examples were provided to analyze and confirm the influence of parameter a,fractional order q,and time delayτon chaos synchronization.The simulation results confirm that the chaotic synchronization is affected by a,q andτ.

Memory Analysis for Memristors and Memristive Recurrent Neural Networks

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers.Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear drift.Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

Finite-time Mittag-Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions

The finite-time Mittag-Leffler synchronization is investigated for fractional-order delayed memristive neural networks(FDMNN)with parameters uncertainty and discontinuous activation functions.The relevant results are obtained under the framework of Filippov for such systems.Firstly,the novel feedback controller,which includes the discontinuous functions and time delays,is proposed to investigate such systems.Secondly,the conditions on finite-time Mittag-Leffler synchronization of FDMNN are established according to the properties of fractional-order calculus and inequality analysis technique.At the same time,the upper bound of the settling time for Mittag-Leffler synchronization is accurately estimated.In addition,by selecting the appropriate parameters of the designed controller and utilizing the comparison theorem for fractional-order systems,the global asymptotic synchronization is achieved as a corollary.Finally,a numerical example is given to indicate the correctness of the obtained conclusions.

Sliding Mode Control of Fractional-Order Memristive System

A sliding mode controller for a fractional-order memristor-based chaotic system is designed to address its problem in stabilization control.Firstly,aphysically realizable fractional-order memristive chaotic system was introduced,which can generate a complex dynamic behavior.Secondly,a sliding mode controller based on sliding mode theory along with Lyapunov stability theory was designed to guarantee the occurrence of the sliding motion.Furthermore,in order to demonstrate the feasibility of the controller,a condition was derived with the designed controller＇s parameters,and the stability analysis of the controlled system was tested.A theoretical analysis shows that,under suitable condition,the fractional-order memristive system with a sliding mode controller comes to a steady state.Finally,numerical simulations are shown to verify the theoretical analysis.It is shown that the proposed sliding mode method exhibits a considerable improvement in its applications in a fractional-order memristive system.

FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and dynamically analyzed,and implemented on FPGA.Then,a new pseudo-random number generator(PRNG)based on MHNN is proposed.The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of PRNG.The experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming language.The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high performance.Finally,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).

Embedding any desired number of coexisting attractors in memristive system

A simple variable-boostable system is selected as the structure for hosting an arbitrarily defined memristor for chaos producing.The derived three-dimensional(3-D)memristive chaotic system shows its distinct property of offset,amplitude and frequency control.Owing its merits any desired number of coexisting attractors are embedded by means of attractor doubling and self-reproducing based on function-oriented offset boosting.In this circumstance two classes of control gates are found:one determines the number of coexisting attractors resorting to the independent offset controller while the other is the initial condition selecting any one of them.Circuit simulation gives a consistent output with theoretically predicted embedded attractors.

Flexible Memristive Devices Based on Graphene Quantum-Dot Nanocomposites

Artificial neural networks(ANNs)are attracting attention for their high performance in various fields,because increasing the network size improves its functioning.Since large-scale neural networks are difficult to implement on custom hardware,a two-dimensional(2D)structure is applied to an ANN in the form of a crossbar.We demonstrate a synapse crossbar device from recent research by applying a memristive system to neuromorphic chips.The system is designed using two-dimensional structures,graphene quantum dots(GQDs)and graphene oxide(GO).Raman spectrum analysis results indicate a D-band of 1421 cm^(−1) that occurs in the disorder;band is expressed as an atomic characteristic of carbon in the sp2 hybridized structure.There is also a G-band of 1518 cm^(−1) that corresponds to the graphite structure.The G bands measured for RGO-GQDs present significant GQD edge-dependent shifts with position.To avoid an abruptly-formed conduction path,effect of barrier layer on graphene/ITO interface was investigated.We confirmed the variation in the nanostructure in the RGO-GQD layers by analyzing them using HR-TEM.After applying a negative bias to the electrode,a crystalline RGO-GQD region formed,which a conductive path.Especially,a synaptic array for a neuromorphic chip with GQDs applied was demonstrated using a crossbar array.

The memristive conductance studying of an Au/Ti_nO_(2n-1) memristor device based on oxygen vacancy drifting

With the insight of memristor researsh growing continuously,many semiconductor material has been manufactured to various kinds of memristive device.Yet the stabilization of memristive performance haven’t been fulfilled,the fathom of memristive-acting mechanism still not being generalized.To put a futher move on low-consuming and high-stable memristive conductance device,we built a high stable double-pair-electrode device,based on the fabrication of TiO_(2-x),which has been generally applied as a n-type semiconductor.Under the constant-repeating cyclic voltammerty;we nailed the memristive quality of our mental/semiconductor thin film device.Moreover,through multifarious analytical processes based on our doping,filming growing path,we build a rational model for our memristor‘s memristive conductance mechanism,which indicated the carrier motion and electron tunnel following the biasing voltage.Our work exhibited a new type of TiO_(2-x)-based memristor,and emerged a new way to explicate the single-stage-switching memristive feature,which might initiate a new guiding ideology in semiconductor memristor’s studying.

Image encryption based on a novel memristive chaotic system,Grain-128a algorithm and dynamic pixel masking

In this paper,we first propose a memristive chaotic system and implement it by circuit simulation.The chaotic dynamics and various attractors are analysed by using phase portrait,bifurcation diagram,and Lyapunov exponents.In particular,the system has robust chaos in a wide parameter range and the initial value space,which is favourable to the security communication application.Consequently,we further explore its application in image encryption and present a new scheme.Before image processing,the external key is protected by the Grain-128a algorithm and the initial values of the memristive system are updated with the plain image.We not only perform random pixel extraction and masking with the chaotic cipher,but also use them as control parameters for Brownian motion to obtain the permutation matrix.In addition,multiplication on the finite field GF(2^(8))is added to further enhance the cryptography.Finally,the simulation results verify that the proposed image encryption scheme has better performance and higher security,which can effectively resist various attacks.

Review of resistive switching mechanisms for memristive neuromorphic devices

Memristive devices have attracted intensive attention in developing hardware neuromorphic computing systems with high energy efficiency due to their simple structure,low power consumption,and rich switching dynamics resembling biological synapses and neurons in the last decades.Fruitful demonstrations have been achieved in memristive synapses neurons and neural networks in the last few years.Versatile dynamics are involved in the data processing and storage in biological neurons and synapses,which ask for carefully tuning the switching dynamics of the memristive emulators.Note that switching dynamics of the memristive devices are closely related to switching mechanisms.Herein,from the perspective of switching dynamics modulations,the mainstream switching mechanisms including redox reaction with ion migration and electronic effect have been systemically reviewed.The approaches to tune the switching dynamics in the devices with different mechanisms have been described.Finally,some other mechanisms involved in neuromorphic computing are briefly introduced.