A Memristor Device Model

Yakopcic, Chris; Taha, Tarek M.; Subramanyam, Guru; Pino, Robinson E.; Rogers, Stanley

doi:10.1109/led.2011.2163292

Cited by 271 publications

(177 citation statements)

References 13 publications

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“…2(f ) during the electroforming and SET process below which the network resistance remains unchanged. The threshold voltage is generally considered as a definite value which is uncorrelated to the device initial states and types of external electrical stimulus in other memristor device models [20,21]. However, in our TiO 2 solid-state RRAM device, we find the threshold voltage is pulse-duration-dependent with sorts of stochastic features [15].…”

Section: Resultsmentioning

confidence: 64%

A memristor random circuit breaker model accounting for stimulus thermal accumulation

Xing

Tian

et al. 2016

IEICE Electron. Express

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Random circuit breaker (RCB) model is a powerful tool to investigate the formation and rupture processes of conductive filaments which occur in unipolar memristor devices. However, the existing RCB models do not integrate the time and thermal parameters, which downgrades significantly the model accuracy in emulating the dynamics of conductive filaments under pulse stimulus. Meanwhile, current research lacks detailed discussions about the above-mentioned problems. In this paper, a SPICEbased optimized RCB model is proposed to explore the unipolar resistive switching characteristics of memristor devices under pulse stimulus. Compared with the original RCB model, the set and reset transitions of each breaker in the proposed model are assumed to be dominated by the thermal heat, which introduces time variable by cascading the thermal equivalent circuits on the original main breaker network and thus allows to explore the filament formation and rupture dynamics along with time. It can simulate all unipolar memristor device operations realized by the original RCB model and is accurate enough to interpret effectively the novel features arising from pulse stimulus such as nonlinear current-voltage relation, multi-states storage capacity, etc.

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Section: Resultsmentioning

confidence: 64%

A memristor random circuit breaker model accounting for stimulus thermal accumulation

Xing

Tian

et al. 2016

IEICE Electron. Express

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“…The parameters are set to:V p = 1.2 V , V n = 0.6 V , A p = 5, A n = 30, x p = 0.7, x n = 0.8, α p = 4, α n = 24, a 1 = 2.3e −4 , a 2 = 3.8e −4 , b = 0.04, according to [5]. It should be noted that as the nanowire width is scaled from 50 nm as reported in [6] to 10 nm, parameter b is correspondingly adjusted to 1/25 of the original value.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…At present, many circuit models for memristor have been proposed. Here, we adopt the model reported in [5] as the base of our approach, because this model depicts several details of the actual devices' characteristics, and can be applied to a wide range of memristors. In this model, the I-V characteristic of memristor is expressed as follows:…”

Section: Modeling Of Nanocrossbar and Memristormentioning

confidence: 99%

Multi-level programming of memristor in nanocrossbar

Zhu

Wu²,

Tang

et al. 2013

IEICE Electron. Express

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Abstract:Utilizing memristor to obtain multi-level memory in nano-crossbar is a promising approach to enhance the memory density. In this paper, we proposed a solution for multi-level programming of memristor in nanocrossbar, which can be implemented on nanocrossbar without the need for extra selective devices. Meanwhile, using a general device model, this solution is demonstrated to be adaptive to a wide range of memristors that have been experimentally fabricated through HSPICE simulation.

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“…It is noted that all of them are functions only in the state variable (except Biolek's, which is also function of the sign, not the magnitude, of the current). Therefore, none of these window functions takes into account the threshold effect (the minimum voltage [7], [15], or current [16], that is needed for the memristance effect to appear). If the applied voltage (or device current) is below the threshold level, no noticeable change in memristance can appear.…”

Section: Window Function Modelsmentioning

confidence: 99%

Memristor model based on fuzzy window function

Abdel-Kader

Abuelenin

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-Memristor (memory-resistor) is the fourth passive circuit element. We introduce a memristor model based on a fuzzy logic window function. Fuzzy models are flexible, which enables the capture of the pinched hysteresis behavior of the memristor. The introduced fuzzy model avoids common problems associated with window-function based memristor models, such as the terminal state problem, and the symmetry issues. The model captures the memristor behavior with a simple rule-base which gives an insight of how memristors work. Because of the flexibility offered by the fuzzy system, shape and distribution of input and output membership functions can be tuned to capture the behavior of various real memristors.

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A Memristor Device Model

Cited by 271 publications

References 13 publications

A memristor random circuit breaker model accounting for stimulus thermal accumulation

A memristor random circuit breaker model accounting for stimulus thermal accumulation

Multi-level programming of memristor in nanocrossbar

Memristor model based on fuzzy window function

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