Picosecond Time-Scale Resistive Switching Monitored in Real-Time

Csontos, Miklós; Horst, Yannik; Olalla, Nadia Jimenez; Koch, Ueli; Shorubalko, Ivan; Halbritter, András; Leuthold, Juerg

doi:10.29363/nanoge.neumatdecas.2023.026

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…Under these conditions, it is clearly impossible to directly study the RS time (t RS ) between different resistive states, which can be significantly less than 50 ns. There are some approaches to circumvent this obstacle and measure switching in the picosecond range (almost) directly; 58 however, this requires a special design and geometry of the memristor, applicable only for RS kinetics measurement. Meanwhile, in this study, the main goal was to investigate switching time in a real device.…”

Section: Resultsmentioning

confidence: 99%

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)_x(LiNbO₃)_100−x nanocomposite memristors

Iliasov,

Matsukatova,

Emelyanov

et al. 2024

Nanoscale Horiz.

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Section: Resultsmentioning

confidence: 99%

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)_x(LiNbO₃)_100−x nanocomposite memristors

Iliasov,

Matsukatova,

Emelyanov

et al. 2024

Nanoscale Horiz.

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“…[28] Namely, higher voltages are required for switching at higher sweep rate, especially for the larger switching times observed for reset transitions. [29] We should also take into account the high-voltage step for the highest cycling frequency corresponding to 0.4 V, which gives rise to the distorted shape of the I-V curves and a low variability, underestimated due to the limited resolution of measurement circuit.…”

Section: Results For the Rs Series At Fast Ramp Ratesmentioning

confidence: 99%

“…The latter factor becomes more significant with an increase in the cycling frequency, and its effect is superimposed on the basic regularities associated with the finite switching time, especially pronounced for the reset process. [29] All these problems should be accurately addressed in further research and technology optimization with relevance to modern applications of memristors.…”

Section: Discussionmentioning

confidence: 99%

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices

Maldonado

Belov

Koryazhkina

et al. 2022

Physica Status Solidi (a)

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Variability is an inherent property of memristive devices based on the switching of resistance in a simple metal–oxide–metal structure compatible with the standard complementary metal–oxide–semiconductor fabrication process. For each specific structure, the variability should be measured and assessed as both the negative and positive factors for different applications of memristive devices. In this report, it is shown how this variability can be extracted and analyzed for such main parameters of resistive switching as the set and reset voltages/currents and how it depends on the methodology used and experimental conditions. The obtained results should be taken into account in the design and predictive simulation of memristive devices and circuits.

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“…below 1 ns [5], [6], [20]- [23]. Witzleben et al and Csontos et al have observed fast SET and RESET switching behavior down to sub-100 ps for VCM devices [6], [7]. To realize neuromorphic applications, such as STP and LTP, on VCM cells with ultra-short pulses and delays down to sub-100 ps, presents an intriguing challenge.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Conductance States in ReRAM Cells through Analysis of Ultra-Fast Pulse Sequences

Munir,

Schön,

Menzel

et al. 2024

Preprint

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Neuromorphic computing, inspired by the processing capabilities of the brain, aims to overcome the limitations of conventional computing architectures. Valence change memory (VCM), along with other emerging resistive random access memory (ReRAM) devices, is a promising candidate for this endeavor due to its features, including fast write times. Moreover, VCM devices are also suitable for neuromorphic applications such as long-term potentiation (LTP), short-term potentiation (STP), and gradual switching. However, evaluating these schemes on a timescale down to sub-100 ps is challenging. This is because the capacitive current dominates the device current on shorter time scales. Therefore, new methods are required to analyze the response of device current to the application of short voltage pulses with short delays. To address the challenge of dominating capacitive current, this study proposes two methods: the integration method and the reference method. The integration method integrates the current and eliminates capacitive charges across a full charge/discharge cycle, proving to be more reliable for longer pulses and delays exceeding 200 ps. On the other hand, the reference method determines the device current by subtracting the capacitive current from the measured current, using a reference measurement. This method is particularly adept at analyzing shorter pulses and delays below 200 ps. Both methods offer distinct advantages in solving the capacitive current challenge in evaluating VCM devices for neuromorphic applications. This research contributes to a better understanding of the behavior of ReRAM cells under ultra-short pulses and delays.

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Picosecond Time-Scale Resistive Switching Monitored in Real-Time

Cited by 4 publications

References 27 publications

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)_x(LiNbO₃)_100−x nanocomposite memristors

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)_x(LiNbO₃)_100−x nanocomposite memristors

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices

Unveiling Conductance States in ReRAM Cells through Analysis of Ultra-Fast Pulse Sequences

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Picosecond Time-Scale Resistive Switching Monitored in Real-Time

Cited by 4 publications

References 27 publications

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)x(LiNbO3)100−x nanocomposite memristors

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)x(LiNbO3)100−x nanocomposite memristors

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti Memristive Devices

Unveiling Conductance States in ReRAM Cells through Analysis of Ultra-Fast Pulse Sequences

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Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)_x(LiNbO₃)_100−x nanocomposite memristors

Adapted MLP-Mixer network based on crossbar arrays of fast and multilevel switching (Co–Fe–B)_x(LiNbO₃)_100−x nanocomposite memristors

A Statistical Study of Resistive Switching Parameters in Au/Ta/ZrO₂(Y)/Ta₂O₅/TiN/Ti Memristive Devices