Editorial

3 pages, 185 KiB

Open AccessEditorial

Memristive Devices and Systems: Modeling, Properties and Applications

by Chun Sing Lai, Zhekang Dong and Donglian Qi

Electronics 2023, 12(3), 765; https://doi.org/10.3390/electronics12030765 - 02 Feb 2023

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The memristor is considered to be a promising candidate for next-generation computing systems due to its nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance [...] Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

Research

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18 pages, 1995 KiB

Open AccessArticle

A Kind of Optoelectronic Memristor Model and Its Applications in Multi-Valued Logic

by Jiayang Wang, Yuzhe Lin, Chenhao Hu, Shiqi Zhou, Shenyu Gu, Mengjie Yang, Guojin Ma and Yunfeng Yan

Electronics 2023, 12(3), 646; https://doi.org/10.3390/electronics12030646 - 28 Jan 2023

Cited by 1 | Viewed by 1348

Abstract

Memristors have been proved effective in intelligent computing systems owing to the advantages of non-volatility, nanometer size, low power consumption, compatibility with traditional CMOS technology, and rapid resistance transformation. In recent years, considerable work has been devoted to the question of how to [...] Read more.

Memristors have been proved effective in intelligent computing systems owing to the advantages of non-volatility, nanometer size, low power consumption, compatibility with traditional CMOS technology, and rapid resistance transformation. In recent years, considerable work has been devoted to the question of how to design and optimize memristor models with different structures and physical mechanisms. Despite the fact that the optoelectronic effect inevitably makes the modelling process more complex and challenging, relatively few research works are dedicated to optoelectronic memristor modelling. Based on this, this paper develops an optoelectronic memristor model (containing mathematical model and circuit model). Moreover, the composite memristor circuit (series- and parallel-connected configuration) with a rotation mechanism is discussed. Further, a multi-valued logic circuit is designed, which is capable of performing multiple logic functions from 0–1, verifying the validity and effectiveness of the established memristor model, as well as opening up a new path for the circuit implementation of fuzzy logic. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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17 pages, 10824 KiB

Open AccessFeature PaperArticle

Two-Neuron Based Memristive Hopfield Neural Network with Synaptic Crosstalk

by Rong Qiu, Yujiao Dong, Xin Jiang and Guangyi Wang

Electronics 2022, 11(19), 3034; https://doi.org/10.3390/electronics11193034 - 23 Sep 2022

Cited by 5 | Viewed by 1459

Abstract

Synaptic crosstalk is an important biological phenomenon that widely exists in neural networks. The crosstalk can influence the ability of neurons to control the synaptic weights, thereby causing rich dynamics of neural networks. Based on the crosstalk between synapses, this paper presents a [...] Read more.

Synaptic crosstalk is an important biological phenomenon that widely exists in neural networks. The crosstalk can influence the ability of neurons to control the synaptic weights, thereby causing rich dynamics of neural networks. Based on the crosstalk between synapses, this paper presents a novel two-neuron based memristive Hopfield neural network with a hyperbolic memristor emulating synaptic crosstalk. The dynamics of the neural networks with varying memristive parameters and crosstalk weights are analyzed via the phase portraits, time-domain waveforms, bifurcation diagrams, and basin of attraction. Complex phenomena, especially coexisting dynamics, chaos and transient chaos emerge in the neural network. Finally, the circuit simulation results verify the effectiveness of theoretical analyses and mathematical simulation and further illustrate the feasibility of the two-neuron based memristive Hopfield neural network hardware. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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19 pages, 10888 KiB

Open AccessArticle

A Passive but Local Active Memristor and Its Complex Dynamics

by Fupeng Li, Jingbiao Liu, Wei Zhou, Yujiao Dong, Peipei Jin, Jiajie Ying and Guangyi Wang

Electronics 2022, 11(12), 1843; https://doi.org/10.3390/electronics11121843 - 09 Jun 2022

Cited by 2 | Viewed by 1496

Abstract

This paper proposes a globally passive but locally active memristor, which has three stable equilibrium points and two unstable equilibrium points, exhibiting two stable locally active regions and four unstable locally active regions. We find that when the memristor operates in a stable [...] Read more.

This paper proposes a globally passive but locally active memristor, which has three stable equilibrium points and two unstable equilibrium points, exhibiting two stable locally active regions and four unstable locally active regions. We find that when the memristor operates in a stable local active region, the memristor-based second-order circuit with a parallel capacitor or a series inductor can produce periodic oscillation. Moreover, the memristor-based third-order circuit with two energy storage elements, a capacitor and an inductor, can produce complex chaotic oscillation, forming the simplest chaotic circuit. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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17 pages, 5903 KiB

Open AccessArticle

Complex Oscillations of Chua Corsage Memristor with Two Symmetrical Locally Active Domains

by Jiajie Ying, Yan Liang, Fupeng Li, Guangyi Wang and Yiran Shen

Electronics 2022, 11(4), 665; https://doi.org/10.3390/electronics11040665 - 21 Feb 2022

Cited by 5 | Viewed by 1663

Abstract

This paper proposes a modified Chua Corsage Memristor endowed with two symmetrical locally active domains. Under the DC bias voltage in the locally active domains, the memristor with an inductor can construct a second-order circuit to generate periodic oscillation. Based on the theories [...] Read more.

This paper proposes a modified Chua Corsage Memristor endowed with two symmetrical locally active domains. Under the DC bias voltage in the locally active domains, the memristor with an inductor can construct a second-order circuit to generate periodic oscillation. Based on the theories of the edge of chaos and local activity, the oscillation mechanism of the symmetrical periodic oscillations of the circuit is revealed. The third-order memristor circuit is constructed by adding a passive capacitor in parallel with the memristor in the second-order circuit, where symmetrical periodic oscillations and symmetrical chaos emerge either on or near the edge of chaos domains. The oscillation mechanisms of the memristor-based circuits are analyzed via Domains distribution maps, which include the division of locally passive domains, locally active domains, and the edge of chaos domains. Finally, the symmetrical dynamic characteristics are investigated via theory and simulations, including Lyapunov exponents, bifurcation diagrams, and dynamic maps. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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13 pages, 4120 KiB

Open AccessArticle

VO₂ Carbon Nanotube Composite Memristor-Based Cellular Neural Network Pattern Formation

by Yiran Shen and Guangyi Wang

Electronics 2021, 10(10), 1198; https://doi.org/10.3390/electronics10101198 - 18 May 2021

Cited by 2 | Viewed by 2083

Abstract

A cellular neural network (CNN) based on a VO₂ carbon nanotube memristor is proposed in this paper. The device is modeled by SPICE at first, and then the cell dynamic characteristics based on the device are analyzed. It is pointed out that [...] Read more.

A cellular neural network (CNN) based on a VO₂ carbon nanotube memristor is proposed in this paper. The device is modeled by SPICE at first, and then the cell dynamic characteristics based on the device are analyzed. It is pointed out that only when the cell is at the sharp edge of chaos can the cell be successfully awakened after the CNN is formed. In this paper, we give the example of a 5 × 5 CNN, set specific initial conditions and observe the formed pattern. Because the generated patterns are affected by the initial conditions, the cell power supply can be pre-programmed to obtain specific patterns, which can be applied to the future information processing system based on complex space–time patterns, especially in the field of computer vision. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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15 pages, 9470 KiB

Open AccessArticle

Characteristic Analysis of Fractional-Order Memristor-Based Hypogenetic Jerk System and Its DSP Implementation

by Chuan Qin, Kehui Sun and Shaobo He

Electronics 2021, 10(7), 841; https://doi.org/10.3390/electronics10070841 - 01 Apr 2021

Cited by 20 | Viewed by 1734

Abstract

In this paper, a fractional-order memristive model with infinite coexisting attractors is investigated. The numerical solution of the system is derived based on the Adomian decomposition method (ADM), and its dynamic behaviors are analyzed by means of phase diagrams, bifurcation diagrams, Lyapunov exponent [...] Read more.

In this paper, a fractional-order memristive model with infinite coexisting attractors is investigated. The numerical solution of the system is derived based on the Adomian decomposition method (ADM), and its dynamic behaviors are analyzed by means of phase diagrams, bifurcation diagrams, Lyapunov exponent spectrum (LEs), dynamic map based on SE complexity and maximum Lyapunov exponent (MLE). Simulation results show that it has rich dynamic characteristics, including asymmetric coexisting attractors with different structures and offset boosting. Finally, the digital signal processor (DSP) implementation verifies the correctness of the solution algorithm and the physical feasibility of the system. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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13 pages, 1820 KiB

Open AccessArticle

Toward Reliable Compact Modeling of Multilevel 1T-1R RRAM Devices for Neuromorphic Systems

by Emilio Pérez-Bosch Quesada, Rocío Romero-Zaliz, Eduardo Pérez, Mamathamba Kalishettyhalli Mahadevaiah, John Reuben, Markus Andreas Schubert, Francisco Jiménez-Molinos, Juan Bautista Roldán and Christian Wenger

Electronics 2021, 10(6), 645; https://doi.org/10.3390/electronics10060645 - 11 Mar 2021

Cited by 29 | Viewed by 3353

Abstract

In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by [...] Read more.

In this work, three different RRAM compact models implemented in Verilog-A are analyzed and evaluated in order to reproduce the multilevel approach based on the switching capability of experimental devices. These models are integrated in 1T-1R cells to control their analog behavior by means of the compliance current imposed by the NMOS select transistor. Four different resistance levels are simulated and assessed with experimental verification to account for their multilevel capability. Further, an Artificial Neural Network study is carried out to evaluate in a real scenario the viability of the multilevel approach under study. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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18 pages, 2908 KiB

Open AccessArticle

History Erase Effect of Real Memristors

by Yiran Shen and Guangyi Wang

Electronics 2021, 10(3), 303; https://doi.org/10.3390/electronics10030303 - 27 Jan 2021

Cited by 3 | Viewed by 1946

Abstract

Different from the static (power-off) nonvolatile property of a memristor, the history erase effect of a memristor is a dynamic characteristic, which means that under the excitation of switching or different signals, the memristor can forget its initial value and reach a unique [...] Read more.

Different from the static (power-off) nonvolatile property of a memristor, the history erase effect of a memristor is a dynamic characteristic, which means that under the excitation of switching or different signals, the memristor can forget its initial value and reach a unique stable state. The stable state is determined only by the excitation signal and has nothing to do with its initial state. The history erase effect is a desired effect in memristor applications such as memory. It can simplify the complexity of the writing circuit and improve the storage speed. If the memristor’s response depends on the initial state, a state reset operation is required before each writing operation. Therefore, it is of great theoretical and practical significance to judge whether the memristor has a history erase effect. Based on the study of the history erase effect of real memristors, this paper focuses on the history erase effect of a Hewlett-Packard (HP) TiO₂ memristor and the Self-Directed Channel (SDC) memristor of Knowm Company. The DC and AC responses of the HP TiO₂ memristor are given, and it is pointed out that there is no AC history erase effect. However, considering the parasitic memcapacitance effect, it is found that it has the effect. Based on the theoretical model of the SDC memristor, its history erase properties with and without considering parasitic effects are studied. It should be noted that this study method can be useful for other materials such as Al₂O₃ and MoS₂. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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Review

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25 pages, 43344 KiB

Open AccessReview

Memristive System Based Image Processing Technology: A Review and Perspective

by Xiaoyue Ji, Zhekang Dong, Guangdong Zhou, Chun Sing Lai, Yunfeng Yan and Donglian Qi

Electronics 2021, 10(24), 3176; https://doi.org/10.3390/electronics10243176 - 20 Dec 2021

Cited by 7 | Viewed by 4096

Abstract

As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of [...] Read more.

As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperation. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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21 pages, 6517 KiB

Open AccessReview

Memcapacitor and Meminductor Circuit Emulators: A Review

by Francisco J. Romero, Akiko Ohata, Alejandro Toral-Lopez, Andres Godoy, Diego P. Morales and Noel Rodriguez

Electronics 2021, 10(11), 1225; https://doi.org/10.3390/electronics10111225 - 21 May 2021

Cited by 22 | Viewed by 5879

Abstract

In 1971, Prof. L. Chua theoretically introduced a new circuit element, which exhibited a different behavior from that displayed by any of the three known passive elements: the resistor, the capacitor or the inductor. This element was called memristor, since its behavior corresponded [...] Read more.

In 1971, Prof. L. Chua theoretically introduced a new circuit element, which exhibited a different behavior from that displayed by any of the three known passive elements: the resistor, the capacitor or the inductor. This element was called memristor, since its behavior corresponded to a resistor with memory. Four decades later, the concept of mem-elements was extended to the other two circuit elements by the definition of the constitutive equations of both memcapacitors and meminductors. Since then, the non-linear and non-volatile properties of these devices have attracted the interest of many researches trying to develop a wide range of applications. However, the lack of solid-state implementations of memcapacitors and meminductors make it necessary to rely on circuit emulators for the use and investigation of these elements in practical implementations. On this basis, this review gathers the current main alternatives presented in the literature for the emulation of both memcapacitors and meminductors. Different circuit emulators have been thoroughly analyzed and compared in detail, providing a wide range of approaches that could be considered for the implementation of these devices in future designs. Full article

(This article belongs to the Special Issue Memristive Devices and Systems: Modelling, Properties & Applications)

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