The DOI matches its Crossref record; 167 of the 168 references it cites were found in a registry or an index.
Memristive devices for computing
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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In the citing paper
“Related challenges are achieving higher computational precision [50, 51], inherent stochastic switching of devices and their impacts on reliability [52, 53], and levels of parallelism [54].”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [54]
In the citing paper
“but the results can be generalized to other cation (5) or anion (e.g. oxygen vacancy in oxides) (6) based memristive systems as well.”
From Stochastic memristive devices for computing and neuromorphic applications (2013) · cites this paper as [6]
Citations
Cites
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Cited by
Sorted by citations
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Self-citationIn-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298
“1b, unipolar RRAMs have also been presented, where the set and reset processes both occur under the same voltage polarity because of the dominant role of Joule heating in creating and dissolving the CF 20,21 .”
BackgroundThe future of electronics based on memristive systems
Zidan, M. A., Strachan, J. P., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-017-0006-8 · Cited by 2,138
van de Burgt, Y., Lubberman, E., Fuller, E. J., Keene, S. T., Faria, G. C., Agarwal, S., Marinella, M. J., Alec Talin, A., & Salleo, A. (2017) · Nature Materials · doi:10.1038/nmat4856 · Cited by 1,872
Memristive crossbar arrays for brain-inspired computing
Xia, Q., & Yang, J. J. (2019) · Nature Materials · doi:10.1038/s41563-019-0291-x · Cited by 1,800
Self-citation
Metadata
- 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.
- Venue
- Nature Nanotechnology
- Published
- 2012-12-27
- Volume
- 8
- Issue
- 1
- Pages
- 13-24
- ISSN
- 1748-3387 (ISSN-L)
- 1748-3395
- 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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