A Versatile Memristor Model With Nonlinear Dopant Kinetics

Prodromakis, Themis; Peh, Boon Pin; Papavassiliou, Christos; Toumazou, C.

doi:10.1109/ted.2011.2158004

Cited by 395 publications

(224 citation statements)

References 20 publications

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“…Simple models of idealized memristors such as in [11][12][13][14] may serve to perform numerical experiments to support circuit-theoretical results or obtain 'small-signal' memristor responses.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of large memristive networks

Biolek

Kolka

Biolková

et al. 2017

Circuit Theory & Apps

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SUMMARYThe paper deals with the modeling of memristors operating in extremely large memristive networks such as crossbar structures for memory and computational circuits, memristor-based neural networks or circuits for massively parallel analog computations. Because the non-convergence and other numerical problems increase with increasing complexity of the simulated circuit, suitable models of the individual memristors need to be choicely developed and optimized. Three different models are considered, each representing a specific trade-off between speed and accuracy. Benchmark circuits for testing the applications of various complexities are used for the transient analysis in HSPICE. It is shown how the models can be modified to minimize the simulation time and improve the convergence.

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Section: Introductionmentioning

confidence: 99%

Modeling and simulation of large memristive networks

Biolek

Kolka

Biolková

et al. 2017

Circuit Theory & Apps

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“…Recently, devices based on different materials have been introduced [5][6][7][8][9]. In addition, several models for memristors has been introduced [10][11][12][13][14][15][16]. Since the first reported use of the memristor, it received a significant of attention in the research community.…”

Section: Introductionmentioning

confidence: 99%

Memristor-based memory: The sneak paths problem and solutions

Zidan

Fahmy

Hussain

et al. 2013

Microelectronics Journal

381

195

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In this paper, we investigate the read operation of memristor-based memories. We analyze the sneak paths problem and provide a noise margin metric to compare the various solutions proposed in the literature. We also analyze the power consumption associated with these solutions. Moreover, we study the effect of the aspect ratio of the memory array on the sneak paths. Finally, we introduce a new technique for solving the sneak paths problem by gating the memory cell using a three-terminal memistor device.

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“…Following this, MATLAB and SPICE models were presented for simulation [3][4][5] and more detailed analyses were presented [6,7]. Many more memristor models were then presented by means of available data relating to the dynamic behavior of many different devices to obtain models behaving like them [8,9]. They used a lot of devices, macromodels, and emulators with circuit components or MATLAB and SPICE tools [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Many more memristor models were then presented by means of available data relating to the dynamic behavior of many different devices to obtain models behaving like them [8,9]. They used a lot of devices, macromodels, and emulators with circuit components or MATLAB and SPICE tools [8][9][10]. In order to enhance functionality, obtain a more compact structure, and reduce power consumption, a lot of studies have been conducted on memristors for many analogue and digital circuits, including cellular neural networks (CNN) [11], recurrent neural networks (RNN) [12], Schmitt triggers, difference amplifiers [13,14], ultrawide band receivers [15], adaptive filters [16] and oscillators [17], field programmable nanowire interconnect (FPNI) design [18], field programmable gate array (FPGA) design [19,20], and CMOS design [21,22].…”

Section: Introductionmentioning

confidence: 99%

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Uçar¹,

Yavşan²

2016

Turk J Elec Eng & Comp Sci

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Abstract:As the behavior of a chaotic Chua's circuit is nonstationary and inherently noisy, it is regarded as one of the most challenging applications. One of the fundamental problems in the prediction of the behavior of a chaotic Chua's circuit is to model the circuit with high accuracy. The current paper presents a novel method based on multiple extreme learning machine (ELM) models to learn the chaotic behavior of the four elements canonical Chua's circuit containing a memristor instead of a nonlinear resistor only by using the state variables as the input. In the proposed method four ELM models are used to estimate the state variables of the circuit. ELMs are first trained by using the data spoilt by noise obtained from MATLAB models of a memristor and Chua's circuit. A multistep-ahead prediction is then carried out by the trained ELMs in the autonomous mode. All attractors of the circuit are finally reconstructed by the outputs of the models. The results of the four ELMs are compared to those of multiple linear regressors (MLRs) and support vector machines (SVMs) in terms of scatter plots, power spectral density, training time, prediction time, and some statistical error measures. Extensive numerical simulations results show that the proposed system exhibits a highly accurate multistep iterated prediction consisting of 1104 steps of the chaotic circuit. Consequently, the proposed model can be considered a promising and powerful tool for modeling and predicting the behavior of Chua's circuit with excellent performance, reducing training time, testing time, and practically realization probability.

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A Versatile Memristor Model With Nonlinear Dopant Kinetics

Cited by 395 publications

References 20 publications

Modeling and simulation of large memristive networks

Modeling and simulation of large memristive networks

Memristor-based memory: The sneak paths problem and solutions

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

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