Simulation of memristor switching time series in response to spike-like signal

Zhevnenko, Dmitry; Meshchaninov, Fedor; Kozhevnikov, Vladislav; Shamin, E. S.; Belov, A. I.; Gerasimova, S. A.; Гусейнов, Д. В.; Mikhaylov, A. N.; Горнев, Е. С.

doi:10.1016/j.chaos.2020.110382

Cited by 20 publications

(20 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, linear and nonlinear models show divergent results: both different positions of attractors and their complete absence. It is shown that for the tests given in the article the best results with respect to the chosen comparison parameters were shown by the models presented in the articles [10,23].…”

Section: Discussionmentioning

confidence: 99%

“…In this paper we performed a feature meta-analysis of memristor based on lowdimensional materials. Based on the results obtained, we conducted a study comparing linear drift models [2], nonlinear drift models [20][21][22] and threshold adaptive models [10,23,24]. A global random search algorithm was used to solve the optimization problem, and an error function with the inclusion of a regularizer was developed to estimate the loop features.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, we have previously explicitly demonstrated the possibility to obtain attractors for the Yakopcic's and MMM models [10]. Also, the original paper by Slipko and Pershin [49] considers dynamic attractors for some nonlinear drift models with different window functions.…”

Section: Dynamic Attractorsmentioning

confidence: 99%

“…Depending on the material of the functional layer and the design features of the memristive element, their main drawbacks vary from structure to structure, but most of them are determined by the random nature of the processes occurring inside the device [7][8][9][10][11]. One important direction in the development of memristors is the use of promising lowdimensional materials to increase the stability of device characteristics and form its new properties [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we performed a feature meta-analysis of memristor based on lowdimensional materials. Based on the results obtained, we conducted a study comparing linear drift models [2], nonlinear drift models [20][21][22] and threshold adaptive models [10,23,24]. In addition to the synthetic sample of the volt-ampere characteristics built on the basis of the features of the devices highlighted in various studies, the results of experiments in the study of the graphene oxide memristor characteristics, first presented in [25], are also used to investigate the descriptive power of the models.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Study of the Applicability of Existing Compact Models to the Simulation of Memristive Structures Characteristics on Low-Dimensional Materials

et al. 2021

View full text Add to dashboard Cite

The use of low-dimensional materials is a promising approach to improve the key characteristics of memristors. The development process includes modeling, but the question of the most common compact model applicability to the modeling of device characteristics with the inclusion of low-dimensional materials remains open. In this paper, a comparative analysis of linear and nonlinear drift as well as threshold models was conducted. For this purpose, the assumption of the relationship between the results of the optimization of the volt–ampere characteristic loop and the descriptive ability of the model was used. A global random search algorithm was used to solve the optimization problem, and an error function with the inclusion of a regularizer was developed to estimate the loop features. Based on the characteristic features derived through meta-analysis, synthetic volt–ampere characteristic contours were built and the results of their approximation by different models were compared. For every model, the quality of the threshold voltage estimation was evaluated, the forms of the memristor potential functions and dynamic attractors associated with experimental contours on graphene oxide were calculated.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Dynamic Attractorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this paper we performed a feature meta-analysis of memristor based on lowdimensional materials. Based on the results obtained, we conducted a study comparing linear drift models [2], nonlinear drift models [20][21][22] and threshold adaptive models [10,23,24]. In addition to the synthetic sample of the volt-ampere characteristics built on the basis of the features of the devices highlighted in various studies, the results of experiments in the study of the graphene oxide memristor characteristics, first presented in [25], are also used to investigate the descriptive power of the models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Study of the Applicability of Existing Compact Models to the Simulation of Memristive Structures Characteristics on Low-Dimensional Materials

et al. 2021

View full text Add to dashboard Cite

show abstract

Artificial sensory system based on memristive devices

Kwon,

Kim,

Kim

et al. 2023

Exploration

View full text Add to dashboard Cite

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real‐time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal‐processing functions such as selective adaption in receptors, leaky integrate‐and‐fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by “artificial receptors,” encoded into spike signals via “artificial neurons,” and integrated and stored through “artificial synapses.” The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high‐performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real‐time detection and processing of environmental information. This review explores the recent advances in memristor‐based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in‐depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor‐based artificial sensory systems are discussed.

show abstract

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices

Maldonado

Belov

Koryazhkina

et al. 2022

Physica Status Solidi (a)

View full text Add to dashboard Cite

Variability is an inherent property of memristive devices based on the switching of resistance in a simple metal–oxide–metal structure compatible with the standard complementary metal–oxide–semiconductor fabrication process. For each specific structure, the variability should be measured and assessed as both the negative and positive factors for different applications of memristive devices. In this report, it is shown how this variability can be extracted and analyzed for such main parameters of resistive switching as the set and reset voltages/currents and how it depends on the methodology used and experimental conditions. The obtained results should be taken into account in the design and predictive simulation of memristive devices and circuits.

show abstract

Simulation of memristor switching time series in response to spike-like signal

Cited by 20 publications

References 42 publications

A Study of the Applicability of Existing Compact Models to the Simulation of Memristive Structures Characteristics on Low-Dimensional Materials

A Study of the Applicability of Existing Compact Models to the Simulation of Memristive Structures Characteristics on Low-Dimensional Materials

Artificial sensory system based on memristive devices

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices

Contact Info

Product

Resources

About

Simulation of memristor switching time series in response to spike-like signal

Cited by 20 publications

References 42 publications

A Study of the Applicability of Existing Compact Models to the Simulation of Memristive Structures Characteristics on Low-Dimensional Materials

A Study of the Applicability of Existing Compact Models to the Simulation of Memristive Structures Characteristics on Low-Dimensional Materials

Artificial sensory system based on memristive devices

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti Memristive Devices

Contact Info

Product

Resources

About

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices