Studying the switching variability in redox-based resistive switching devices

Abbaspour, Elhameh; Menzel, Stephan; Jungemann, C.

doi:10.1007/s10825-020-01537-y

Cited by 12 publications

(6 citation statements)

References 30 publications

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“…The reset current level approaches a steady plateau with only minor fluctuations as the reset-stop voltage is increased. The space between the tip of the conductive filaments and the electrode, which can vary greatly from cycle to cycle, is smaller at lower reset-stop voltage [ 60 ]. Besides, by employing greater reset-stop voltage, all conductive filaments were ruptured, which is responsible for decreasing the OFF-state resistance.…”

Section: Resultsmentioning

confidence: 99%

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

et al. 2022

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For neuromorphic computing and high-density data storage memory, memristive devices have recently gained a lot of interest. So far, memristive devices have suffered from switching parameter instability, such as distortions in resistance values of low- and high-resistance states (LRSs and HRSs), dispersion in working voltage (set and reset voltages), and a small ratio of high and low resistance, among other issues. In this context, interface engineering is a critical technique for addressing the variation issues that obstruct the use of memristive devices. Herein, we engineered a high band gap, low Gibbs free energy Al2O3 interlayer between the HfO2 switching layer and the tantalum oxy-nitride electrode (TaN) bottom electrode to operate as an oxygen reservoir, increasing the resistance ratio between HRS and LRS and enabling multilayer data storage. The Pt/HfO2/Al2O3/TaN memristive device demonstrates analog bipolar resistive switching behavior with a potential ratio of HRS and LRS of > 105 and the ability to store multi-level data with consistent retention and uniformity. On set and reset voltages, statistical analysis is used; the mean values (µ) of set and reset voltages are determined to be − 2.7 V and + 1.9 V, respectively. There is a repeatable durability over DC 1000 cycles, 105 AC cycles, and a retention time of 104 s at room temperature. Quantum conductance was obtained by increasing the reset voltage with step of 0.005 V with delay time of 0.1 s. Memristive device has also displayed synaptic properties like as potentiation/depression and paired-pulse facilitation (PPF). Results show that engineering of interlayer is an effective approach to improve the uniformity, ratio of high and low resistance, and multiple conductance quantization states and paves the way for research into neuromorphic synapses.

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Section: Resultsmentioning

confidence: 99%

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

et al. 2022

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“…Device models that naturally encompass stochasticity do so at the cost of complexity needed to compute the physical scenario in high detail. For example, atomistic KMC simulates switching processes with atomic precision and is inherently stochastic but requires hours of computation per cycle even for small individual cell volumes (e.g., 125 nm 2 Abbaspour et al, 2020 ). At the other end of the spectrum, dynamic models based on numerical solutions of ODE systems are designed to run significantly faster while sometimes aiming to remain physically realistic.…”

Section: Introductionmentioning

confidence: 99%

A high throughput generative vector autoregression model for stochastic synapses

et al. 2022

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By imitating the synaptic connectivity and plasticity of the brain, emerging electronic nanodevices offer new opportunities as the building blocks of neuromorphic systems. One challenge for large-scale simulations of computational architectures based on emerging devices is to accurately capture device response, hysteresis, noise, and the covariance structure in the temporal domain as well as between the different device parameters. We address this challenge with a high throughput generative model for synaptic arrays that is based on a recently available type of electrical measurement data for resistive memory cells. We map this real-world data onto a vector autoregressive stochastic process to accurately reproduce the device parameters and their cross-correlation structure. While closely matching the measured data, our model is still very fast; we provide parallelized implementations for both CPUs and GPUs and demonstrate array sizes above one billion cells and throughputs exceeding one hundred million weight updates per second, above the pixel rate of a 30 frames/s 4K video stream.

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“…It can be seen a larger C2C spread in the HRS current than in the LRS current. [ 61 ] This higher variability is attributed to the variations of the gap distance between the tip of the broken CF and the metal electrode when the reset process is activated. For statistical analysis, set and reset voltages ( V set and V reset ) and currents ( I set and I reset ) are extracted for each RS cycle of the 3000 experimental cycles.…”

Section: Experimental Evidence Of Variability In Resistive Memoriesmentioning

confidence: 99%

“…According to (61), the coefficient B is asymmetric and can be different for positive and negative values of the driving voltage. Therefore, we should use B ¼ B SET for the voltage values at which the set process occurs (for the structure shown in Figure 21 at V > 0) and B ¼ B RES for the voltage values at which the reset process takes place (for the structure shown in Figure 21 at V < 0).…”

Section: On the Correct Introduction Of A Thermal Noise Sourcementioning

confidence: 99%

Variability in Resistive Memories

Roldán

Miranda

Maldonado

et al. 2023

Advanced Intelligent Systems

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Research and development efforts in the nonvolatile memory arena are focused on a reduced set of innovative components, among which we can include memristors. [1,2] Memristors are expected to be key players in the electronics landscape of the coming years largely because of the powerful applications that stand upon their unique features. [1,[3][4][5] The switching mechanisms behind memristors differ significantly depending on the physical properties of the structures and the materials involved. [1,[3][4][5][6][7] To list some of these mechanisms, we can highlight those devices based on phase-change materials, which can be switched reversibly between amorphous and crystalline phases with different electrical resistivity (phasechange memories, PCMs); [8] devices that take advantage of the magnetic and electrical properties exhibited by some materials with different architectures (magnetic RAMs, MRAMs); [9] also structures where materials with switchable electrical polarization give rise to hysteresis curves of the polarization versus electrical field that can be engineered for storing information (ferroelectric FET, FFET); [10] and, finally, resistive RAMs (RRAMs) where the dielectric conduction properties are altered by means of the internal ion movement and concurrent redox reactions used to generate different resistive states. [1,3,11,12]

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Studying the switching variability in redox-based resistive switching devices

Cited by 12 publications

References 30 publications

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

A high throughput generative vector autoregression model for stochastic synapses

Variability in Resistive Memories

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