Cited by 3,885Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesPhase-change materials and chalcogenides

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Memristive devices for computing

Yang, J. Joshua, Strukov, Dmitri B., Stewart, Duncan R.·2013·Nature Nanotechnology

DOI 10.1038/nnano.2012.240

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Abstract

Memristive devices are electrical resistance switches that can retain a state of internal resistance based on the history of applied voltage and current. These devices can store and process information, and offer several key performance characteristics that exceed conventional integrated circuit technology. An important class of memristive devices are two-terminal resistance switches based on ionic motion, which are built from a simple conductor/insulator/conductor thin-film stack. These devices were originally conceived in the late 1960s and recent progress has led to fast, low-energy, high-endurance devices that can be scaled down to less than 10 nm and stacked in three dimensions. However, the underlying device mechanisms remain unclear, which is a significant barrier to their widespread application. Here, we review recent progress in the development and understanding of memristive devices. We also examine the performance requirements for computing with memristive devices and detail how the outstanding challenges could be met.

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Background
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Self-citation
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Self-citation
Memristor based computation-in-memory architecture for data-intensive applications
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Authors: Yang, J. J., Strukov, D. B., & Stewart, D. R.
Affiliations: Hewlett-Packard (United States) (US)
University of California, Santa Barbara (US)
National Research Council Canada (CA)
Corresponding author: Yang, J. Joshua
Strukov, Dmitri B.
Stewart, Duncan R.
ORCID: Yang, J. Joshua
Strukov, Dmitri B.
Venue: Nature Nanotechnology
Publisher: Springer Science and Business Media LLC
Published: 2012-12-27
Volume: 8
Issue: 1
Pages: 13-24
ISSN: 1748-3387 (ISSN-L)
1748-3395
DOI: 10.1038/nnano.2012.240
OpenAlex: W2018774711
Semantic Scholar: 59f65b20279e6153f6caa83c07ada43fac643d34
Cited by: 3,885
References: 168
Citations by year: 2026: 259 · 2025: 245 · 2024: 248 · 2023: 290 · 2022: 307 · 2021: 331 · 2020: 390 · 2019: 336 · 2018: 338 · 2017: 314 · 2016: 299 · 2015: 226 · 2014: 200 · 2013: 99 · 2012: 1
FWCI: 107.49
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature Nanotechnology (publishedVersion)
PubMed (publishedVersion)
NPARC (submittedVersion)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
Physical Sciences › Materials Science › Materials Chemistry › Phase-change materials and chalcogenides
Keywords: memristors, ion dynamics, in-memory computing, device scaling, endurance, energy-efficient computing

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