Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

Christian Acal; David Maldonado; Ana M. Aguilera; Kaichen Zhu; Mario Lanza; Juan Bautista Roldán

doi:10.1021/acsami.2c22617

Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

Christian Acal, David Maldonado, Ana M. Aguilera, Kaichen Zhu, Mario Lanza, Juan Bautista Roldán

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

We present a new methodology to quantify the variability of resistive switching memories. Instead of statistically analyzing few data points extracted from current versus voltage (I-V) plots, such as switching voltages or state resistances, we take into account the whole I-V curve measured in each RS cycle. This means going from a one-dimensional data set to a two-dimensional data set, in which every point of each I-V curve measured is included in the variability calculation. We introduce a new coefficient (named two-dimensional variability coefficient, 2DVC) that reveals additional variability information to which traditional one-dimensional analytical methods (such as the coefficient of variation) are blind. This novel approach provides a holistic variability metric for a better understanding of the functioning of resistive switching memories.

Original language	English (US)
Journal	ACS Applied Materials & Interfaces
DOIs	https://doi.org/10.1021/acsami.2c22617
State	Published - Apr 7 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-04-10
Acknowledgements: The authors thank the support of the Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain), and the FEDER program for Projects B-TIC-624-UGR20, PID2020-113961GB-I00, A-FQM-66-UGR20, and FQM-307. Additionally, the authors acknowledge financial support by the IMAG María de Maeztu Grant CEX2020-001105-M/AEI/10.13039/501100011033. M.L. acknowledges generous support from the King Abdullah University of Science and Technology.

ASJC Scopus subject areas

General Materials Science

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10.1021/acsami.2c22617

https://repository.kaust.edu.sa/bitstream/10754/690910/1/acsami.2c22617.pdf

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@article{ad9c5d3826fa41538d9d1e64cdd0032b,

title = "Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient",

abstract = "We present a new methodology to quantify the variability of resistive switching memories. Instead of statistically analyzing few data points extracted from current versus voltage (I-V) plots, such as switching voltages or state resistances, we take into account the whole I-V curve measured in each RS cycle. This means going from a one-dimensional data set to a two-dimensional data set, in which every point of each I-V curve measured is included in the variability calculation. We introduce a new coefficient (named two-dimensional variability coefficient, 2DVC) that reveals additional variability information to which traditional one-dimensional analytical methods (such as the coefficient of variation) are blind. This novel approach provides a holistic variability metric for a better understanding of the functioning of resistive switching memories.",

author = "Christian Acal and David Maldonado and Aguilera, {Ana M.} and Kaichen Zhu and Mario Lanza and Rold{\'a}n, {Juan Bautista}",

note = "KAUST Repository Item: Exported on 2023-04-10 Acknowledgements: The authors thank the support of the Consejer{\'i}a de Conocimiento, Investigaci{\'o}n y Universidad, Junta de Andaluc{\'i}a (Spain), and the FEDER program for Projects B-TIC-624-UGR20, PID2020-113961GB-I00, A-FQM-66-UGR20, and FQM-307. Additionally, the authors acknowledge financial support by the IMAG Mar{\'i}a de Maeztu Grant CEX2020-001105-M/AEI/10.13039/501100011033. M.L. acknowledges generous support from the King Abdullah University of Science and Technology.",

year = "2023",

month = apr,

day = "7",

doi = "10.1021/acsami.2c22617",

language = "English (US)",

journal = "ACS Applied Materials & Interfaces",

issn = "1944-8244",

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T1 - Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

AU - Acal, Christian

AU - Maldonado, David

AU - Aguilera, Ana M.

AU - Zhu, Kaichen

AU - Lanza, Mario

AU - Roldán, Juan Bautista

N1 - KAUST Repository Item: Exported on 2023-04-10 Acknowledgements: The authors thank the support of the Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain), and the FEDER program for Projects B-TIC-624-UGR20, PID2020-113961GB-I00, A-FQM-66-UGR20, and FQM-307. Additionally, the authors acknowledge financial support by the IMAG María de Maeztu Grant CEX2020-001105-M/AEI/10.13039/501100011033. M.L. acknowledges generous support from the King Abdullah University of Science and Technology.

PY - 2023/4/7

Y1 - 2023/4/7

N2 - We present a new methodology to quantify the variability of resistive switching memories. Instead of statistically analyzing few data points extracted from current versus voltage (I-V) plots, such as switching voltages or state resistances, we take into account the whole I-V curve measured in each RS cycle. This means going from a one-dimensional data set to a two-dimensional data set, in which every point of each I-V curve measured is included in the variability calculation. We introduce a new coefficient (named two-dimensional variability coefficient, 2DVC) that reveals additional variability information to which traditional one-dimensional analytical methods (such as the coefficient of variation) are blind. This novel approach provides a holistic variability metric for a better understanding of the functioning of resistive switching memories.

AB - We present a new methodology to quantify the variability of resistive switching memories. Instead of statistically analyzing few data points extracted from current versus voltage (I-V) plots, such as switching voltages or state resistances, we take into account the whole I-V curve measured in each RS cycle. This means going from a one-dimensional data set to a two-dimensional data set, in which every point of each I-V curve measured is included in the variability calculation. We introduce a new coefficient (named two-dimensional variability coefficient, 2DVC) that reveals additional variability information to which traditional one-dimensional analytical methods (such as the coefficient of variation) are blind. This novel approach provides a holistic variability metric for a better understanding of the functioning of resistive switching memories.

UR - http://hdl.handle.net/10754/690910

UR - https://pubs.acs.org/doi/10.1021/acsami.2c22617

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JO - ACS Applied Materials & Interfaces

JF - ACS Applied Materials & Interfaces

ER -

Holistic Variability Analysis in Resistive Switching Memories Using a Two-Dimensional Variability Coefficient

Abstract

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