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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Related challenges for in-memory computing are achieving higher computational precision, inherent stochastic switching of devices and their impacts on reliability, and levels of parallelism.
“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]
The results obtained with memristive devices based on cation migration inside a solid-state electrolyte can be generalized to other cation or anion based memristive systems.
“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]
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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.
- 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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