Physical mechanism of HfO&lt;inf&gt;2&lt;/inf&gt;-based bipolar resistive random access memory

Chang, Herng-Hua; Li, Hsuan-Chih; C, Liu; Chen, F.; Tsai, Ming-Jinn

doi:10.1109/vtsa.2011.5872253

Cited by 12 publications

(5 citation statements)

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“…In this paper, a different model for the resistive switching in TiN/Ti/HfO 2 /TiN devices without thermal annealing is presented. Our model is compatible with theoretical findings, similar to switching models presented by other groups and able to explain experimentally obtained data. ,,,− Furthermore, the conditions for filament growth from the anode to the cathode are discussed. 2D and 3D models have different advantages.…”

Section: Introductionsupporting

confidence: 91%

Filament Growth and Resistive Switching in Hafnium Oxide Memristive Devices

Dirkmann

Kaiser

Wenger

et al. 2018

ACS Appl. Mater. Interfaces

117

124

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We report on the resistive switching in TiN/Ti/HfO/TiN memristive devices. A resistive switching model for the device is proposed, taking into account important experimental and theoretical findings. The proposed switching model is validated using 2D and 3D kinetic Monte Carlo simulation models. The models are consistently coupled to the electric field and different current transport mechanisms such as direct tunneling, trap-assisted tunneling, ohmic transport, and transport through a quantum point contact have been considered. We find that the numerical results are in excellent agreement with experimentally obtained data. Important device parameters, which are difficult or impossible to measure in experiments, are calculated. This includes the shape of the conductive filament, width of filament constriction, current density, and temperature distribution. To obtain insights in the operation of the device, consecutive cycles have been simulated. Furthermore, the switching kinetics for the forming and set process for different applied voltages is investigated. Finally, the influence of an annealing process on the filament growth, especially on the filament growth direction, is discussed.

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Section: Introductionsupporting

confidence: 91%

Filament Growth and Resistive Switching in Hafnium Oxide Memristive Devices

Dirkmann

Kaiser

Wenger

et al. 2018

ACS Appl. Mater. Interfaces

117

124

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“…As suggested by the experimental data [14], the LRS resistance shows a strong dependence on current. Since the set transition involves a continuous growth of conductive filaments, higher current results in lower final set state resistance.…”

Section: ) Lrs Calculationmentioning

confidence: 54%

“…In the oxide-based RRAM, oxygen vacancies play a crucial role in the resistance switching, but, on the other hand, they also cause undesired resistance fluctuation of RRAM. The positively charged oxygen vacancies are mobile under high field and serve like donor dopants to make RRAM n-type semiconductor [14]. Accordingly, we neglect the impact of holes and only consider the electron drift as the conduction current.…”

Section: A Current Calculation Modulementioning

confidence: 99%

A Predictive Compact Model of Bipolar RRAM Cells for Circuit Simulations

Chiang

Hsu²,

Ding

et al. 2015

IEEE Trans. Electron Devices

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A model of resistive random access memory (RRAM) cells aimed at providing an optimal programming/erase scheme in which the timing and biasing can be accurately optimized is proposed and implemented. To expedite technology development with an emphasis on ICs, a predictive model to capture the physical operation of every memory cell is needed. Although a number of compact RRAM models have been developed, this paper further considers the time-dependent reset process and the heat transfer in the conductive filaments. These phenomena are becoming critical in scaled memory cells and need to be carefully addressed. Due to the physical nature of the model, model parameters can be straightforwardly calibrated, relying on limited measurement data. The compact model is implemented using Verilog-A, and it is flexible for different circuit simulators. Index Terms-Bipolar switching, compact model, resistive random access memory (RRAM).

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“…Causes for these errors include diffusion of oxygen vacancies (leading to state drift) [6], ion strikes [7], [8], and environmental factors [9]. These soft errors can accumulate over time [6], [7], [15] or occur abruptly [8], [9].…”

Section: B Soft Errors In Memristorsmentioning

confidence: 99%

Efficient Error-Correcting-Code Mechanism for High-Throughput Memristive Processing-in-Memory

Leitersdorf¹,

Perach²,

Ronen³

et al. 2021

Preprint

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Inefficient data transfer between computation and memory inspired emerging processing-in-memory (PIM) technologies. Many PIM solutions enable storage and processing using memristors in a crossbar-array structure, with techniques such as memristor-aided logic (MAGIC) used for computation. This approach provides highly-paralleled logic computation with minimal data movement. However, memristors are vulnerable to soft errors and standard error-correcting-code (ECC) techniques are difficult to implement without moving data outside the memory. We propose a novel technique for efficient ECC implementation along diagonals to support reliable computation inside the memory without explicitly reading the data. Our evaluation demonstrates an improvement of over eight orders of magnitude in reliability (mean time to failure) for an increase of about 26% in computation latency.

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Physical mechanism of HfO<inf>2</inf>-based bipolar resistive random access memory

Cited by 12 publications

References 0 publications

Filament Growth and Resistive Switching in Hafnium Oxide Memristive Devices

Filament Growth and Resistive Switching in Hafnium Oxide Memristive Devices

A Predictive Compact Model of Bipolar RRAM Cells for Circuit Simulations

Efficient Error-Correcting-Code Mechanism for High-Throughput Memristive Processing-in-Memory

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