Comprehensive study on unipolar RRAM charge conduction and stochastic features: a simulation approach

Maldonado, David; Gómez‐Campos, F. M.; González, M.B.; Roldán, A.; Jiménez-Molinos, F.; Campabadal, F.; Roldán, Juan B.

doi:10.1088/1361-6463/ac472c

Cited by 5 publications

(12 citation statements)

References 61 publications

(74 reference statements)

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“…CB simulation of an experimental set and reset cycle. Simulated [31] and experimental current versus voltage curves, as can be seen, a good fit is obtained. The snapshots of the CB networks linked to the points highlighted along the curves are also given.…”

Section: (H) We Have Implemented a New Technique Forsupporting

confidence: 60%

“…This computational tool allows the step-by-step description of the formation and rupture processes of CNFs in a 2-D domain. The simulator includes CBs with up to four conductance levels, and it accounts for quantum effects in charge conduction by means of the quantum point contact (QPC) model, as well as for the series resistance and dielectrics with several layers [31]. A 2-D network represents the conduction structure of the dielectric, including the possibility of percolation path formation that represents metallic CNFs that short the electrodes after a successful set process.…”

Section: Simulator Descriptionmentioning

confidence: 99%

“…The snapshots of the CB networks linked to the points highlighted along the curves are also given. The CB network models electric conduction in the dielectric [31]. A high CB resistance represents a dielectric region without defects or metal ions and a low-resistance value stands for a region where a defect or a metal ion facilitates charge conduction; in this manner, the model for current calculation is built for this type of circuit-breaker-based simulators [31].…”

Section: (H) We Have Implemented a New Technique Formentioning

confidence: 99%

“…In order to analyze the switching characteristics in a more comprehensive manner, we have made use of a CB simulator [31]. The simulation tool can be tuned for different technologies, accounting for different resistance levels in the CBs and the voltage thresholds of the CBs.…”

Section: (H) We Have Implemented a New Technique Formentioning

confidence: 99%

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Modeling the Variability of Au/Ti/h-BN/Au Memristive Devices

Roldán

Maldonado

Aguilera-Pedregosa

et al. 2023

IEEE Trans. Electron Devices

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The variability of memristive devices using multilayer hexagonal boron nitride (h-BN) coupled with Ti and Au electrodes (i.e., Au/Ti/h-BN/Au) is analyzed in depth using different numerical techniques. We extract the reset voltage using three different methods, quantify its cycle-tocycle variability, calculate the charge and flux that allows to minimize the effects of electric noise and the inherent stochasticity of resistive switching, describe the device variability using time series analyses to assess the "memory" effect, and employ a circuit breaker simulator to understand the formation and rupture of the percolation paths that produce the switching. We conclude that the cycle-to-cycle variability of the Au/Ti/h-BN/Au devices presented here is higher than that previously observed in Au/h-BN/Au devices, and hence, they may be useful for data encryption.

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Section: (H) We Have Implemented a New Technique Forsupporting

confidence: 60%

Section: Simulator Descriptionmentioning

confidence: 99%

Section: (H) We Have Implemented a New Technique Formentioning

confidence: 99%

Section: (H) We Have Implemented a New Technique Formentioning

confidence: 99%

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Modeling the Variability of Au/Ti/h-BN/Au Memristive Devices

Roldán

Maldonado

Aguilera-Pedregosa

et al. 2023

IEEE Trans. Electron Devices

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“…[17] Another advantage of this model is that it is material-independent, focusing only on the overall behavior of the circuit breakers. Furthermore, the RCB network model explains that the wide distributions of switching voltages [16,[18][19][20][21] are due to the randomly turning on or off of circuit breakers in the network which corresponds to the formation and rupture of a small segment of the conductive filaments. This also explains the intrinsic resistance variability [22] and the different operation modes.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Influence of Metal Oxide Layer Thickness and Defects on Resistive Switching Behavior Through Numerical Modeling

Dias

Ventura

2023

Physica Status Solidi (a)

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The resistive switching dynamics in metal–insulator–metal (MIM) structures may be explained through the formation and rupture of metallic filaments in the dielectric layer. Considerable research has been performed as an attempt to explain the microscopic origins of how regions that are originally insulating can change into conductors. Looking at how these conducting local regions are interconnected, the simulation of these localized switches can help understand how material properties such as thickness and defects influence the switching behavior and parameters. Here, the random circuit breaker (RCB) model is implemented and the dependence of the forming, Set, and Reset voltages on the oxide thickness and defect percentage is compared with experimental data. Only the forming voltage increases with the thickness, showing that, after the first step, the conductive filament formation and rupture occur only at a thinner thickness. It is observed that a higher percentage of initial conductive defects in the oxide layer promotes the forming step.

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