State of the art of metal oxide memristor devices

Mohammad, Baker; Jaoudé, Maguy Abi; Kumar, Vikas; Homouz, Dirar; Nahla, Heba Abu; Al‐Qutayri, Mahmoud; Christoforou, Nicolas

doi:10.1515/ntrev-2015-0029

Cited by 158 publications

(75 citation statements)

References 9 publications

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“…Metal filament formation has been the preferred mechanism to explain resistance switching in inorganic memristors (Van den Hurk et al, 2015;Mohammad et al, 2016;Li et al, 2017;Wang et al, 2018), but also in organic ones (He et al, 2009;Sparvoli et al, 2019). According to this model, when a positive voltage is applied to the active electrode, the material is oxidized electrochemically and the resulting cations migrate through the insulating layer up to reach the cathode.…”

Section: Metallic Filamentary Conductionmentioning

confidence: 99%

Resistive Switching in Graphene Oxide

et al. 2020

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The search and investigation of resistive switching materials, the most consolidated form of solid-state memristors, has become one of the fastest growing areas in the field of electronics. This is not only due to the huge commercial interest in developing the so-called Resistive Random-Access Memories (ReRAMs) but also because resistive switching materials are gathering way to new forms of analog computation. Unlike in the field of traditional electronics technologies, where Silicon has monopolized most of the applications, the area of solid-state memristors is opened to a broad set of candidates that may contribute to unprecedented applications. In particular, the use of organic-based resistive switching materials can provide additional functionalities as structural flexibility for conformal integration or introduce new and cost-effective fabrication technologies. Following this new wave of organic memristive materials, this work aims at reviewing the existing models explaining the origins of resistive switching in Graphene Oxide, one of the most promising contenders on the battlefield of emerging memristive materials due to its low cost and easy processing methods. Within this manuscript, we will revisit the different theories supporting the phenomenology of resistive switching in this material nourishing the discussion with experimental results supporting the three main existing theories.

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Section: Metallic Filamentary Conductionmentioning

confidence: 99%

Resistive Switching in Graphene Oxide

et al. 2020

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“…The considered memristor element is based on HfO 2 material [9,10]. In References [7,8], a model based on physical measurements for transition metal oxide memristors is presented.…”

Section: A Description Of the Proposed Hafnium Dioxide Memristor Modelmentioning

confidence: 99%

“…The optimal values of the fitting model parameters were further used for testing the memristor model in memristor circuits and devices, and for analysis of the basic characteristics, namely current-voltage and state-flux relationships. In Mohammad et al [9], several tentative values of the adjusting parameters α, β, γ, and χ are given. They were applied to start a multi-factor analysis [15,16] of the derived current-voltage characteristics of the hafnium dioxide memristor model.…”

Section: Parameter Estimations Of the Considered Memristor Modelmentioning

confidence: 99%

“…The first physical memristor prototype based on titanium dioxide was created in Hewlett Packard's scientific laboratories by Stanley Williams' research team in 2008 [2,4]. Many scientific papers on memristors and memristor-based circuits have been published and several corresponding memristor models have been offered [6][7][8][9][10]. The linear ionic drift memristor model [2] is applied for low-level voltage signals.…”

Section: Introductionmentioning

confidence: 99%

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A New Simplified Model and Parameter Estimations for a HfO2-Based Memristor †

Mladenov

2020

Technologies

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The purpose of this paper was to propose a complete analysis and parameter estimations of a new simplified and highly nonlinear hafnium dioxide memristor model that is appropriate for high-frequency signals. For the simulations; a nonlinear window function previously offered by the author together with a highly nonlinear memristor model was used. This model was tuned according to an experimentally recorded current-voltage relationship of a HfO 2 memristor. This study offered an estimation of the optimal model parameters using a least squares algorithm in SIMULINK and a methodology for adjusting the model by varying its parameters overbroad ranges. The optimal values of the memristor model parameters were obtained after minimizing the error between the experimental and simulated current-voltage characteristics. A comparison of the obtained errors between the simulated and experimental current-voltage relationships was made. The error derived by the optimization algorithm was a little bit lower than that obtained by the used methodology. To avoid convergence problems; the step function in the considered model was replaced by a differentiable tangent hyperbolic function. A PSpice library model of the HfO 2 memristor based on its mathematical model was created. The considered model was successfully applied and tested in a multilayer memristor neural network with bridge memristor-resistor synapses

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“…This is because their existence has been proposed relatively late (1971 by Chua) [1] , its experimental realization came much later, [2] and the mechanisms for the effect are still not fully understood. [3] In particular, a memristor is a resistive component with an inherent memory. Unlike a static resistor, the resistance of a memristor can dynamically be altered by electrical stimuli, resulting in a hysteresis curve in the current-voltage characteristic.…”

Section: Introductionmentioning

confidence: 99%

Functional Gradient Transparent Conducting Oxide Nanowire Arrays as Monophasic Schottky-Barrier Free Memristors for Bipolar Linear Switching

Herzog¹,

Weitzel²,

Polarz³

2020

Preprint

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<div><div><div><p>One of the fascinating properties of metal-semiconductor Schottky-barriers, which has been observed for some material combinations, is memristive behavior. Memristors are smart, since they can reversibly switch between a low resistance state and a high resistance state. The devices offer a great potential for advanced computing and data storage, including neuromorphic networks and resistive random-access memory. However, as for many other cases, the presence of a real interface (metal - metal oxide) has numerous disadvantages. The realization of interface-free, respectively Schottky-barrier free memristors is highly desirable. The aim of the current paper is the generation of nanowire arrays with each nanorod possessing the same crystal phase (Rutile) and segments only differing in composition. The electric conductivity is realized by segments made of highly-doped antimony tin oxide (ATO) transitioning into pure tin oxide (TO). Complex nanoarchitectures are presented, which include ATO-TO, ATO-TO-ATO nanowires either with a stepwise distribution of antimony or as a graded functional material. The electrical characterization of the materials reveals that the introduction of memristive properties in such structures is possible. The special features observed in voltage-current (IV) curves are correlated to the behavior of mobile oxygen vacancies (VO..) at different values of applied electrical potential.</p></div></div></div>

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State of the art of metal oxide memristor devices

Cited by 158 publications

References 9 publications

Resistive Switching in Graphene Oxide

Resistive Switching in Graphene Oxide

A New Simplified Model and Parameter Estimations for a HfO2-Based Memristor †

Functional Gradient Transparent Conducting Oxide Nanowire Arrays as Monophasic Schottky-Barrier Free Memristors for Bipolar Linear Switching

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