Evaluation Methods for ReRAM Potentiation on Sub-Nanosecond Timescales

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 redox-based 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 plasticity (STP), and gradual switching. However, the evaluation of these schemes on sub-100ps timescales presents significant challenges, as the capacitive current dominates the device current, obscuring the true conductance state. Therefore, robust methods are required to analyze the current response of ReRAM cells to ultrashort pulses and varying delays, which is critical for advanced neuromorphic applications. 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 200ps. 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 200ps. These methods provide effective solutions to the challenge of capacitive current dominance to ultrashort pulses and varying delays.

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Publication Details

Journal
IEEE Transactions on Electron Devices
Published
2024-09-30
DOI
https://doi.org/10.1109/ted.2024.3461676
Citations
6
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.25
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article

Evaluation Methods for ReRAM Potentiation on Sub-Nanosecond Timescales

Daniel Schön, Pascal Stasner, Stephan Menzel, Stefan Wiefels et al.
6 citations
IEEE Transactions on Electron Devices
Advanced Memory and Neural Computing
1.25
article

Evaluation Methods for ReRAM Potentiation on Sub-Nanosecond Timescales

Daniel Schön, Pascal Stasner, Stephan Menzel, Stefan Wiefels, Faisal Munir, Rainer Waser
article en
6 citations

Abstract

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 redox-based 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 plasticity (STP), and gradual switching. However, the evaluation of these schemes on sub-100ps timescales presents significant challenges, as the capacitive current dominates the device current, obscuring the true conductance state. Therefore, robust methods are required to analyze the current response of ReRAM cells to ultrashort pulses and varying delays, which is critical for advanced neuromorphic applications. 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 200ps. 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 200ps. These methods provide effective solutions to the challenge of capacitive current dominance to ultrashort pulses and varying delays.

IEEE Transactions on Electron DevicesVol. 71(11)
X-Fab (Germany) (DE), RWTH Aachen University (DE)
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
1.25
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