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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“(1) Schottky emission: thermally activated electrons injected over the barrier into”
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Giant tunnelling magnetoresistance at room temperature with MgO (100) tunnel barriers
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Memristive switching mechanism for metal/oxide/metal nanodevices
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Lee, H. Y. et al. in Int. Electron Devices Meeting 2010 IEDM 2010. IEEE Int. 19.7.1–19.7.4 (IEEE, 2010).
Widrow, B. An adaptive “ADALINE” neuron using chemical “Memistors”. Stanford Electronics Laboratories Technical Report No. 1553–2 (1960).
Strukov, D. B. & Likharev, K. K. Reconfigurable nano-crossbar architectures, in Nanoelectronics (ed. Waser, R.) (in the press, 2012).

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BiFeO3epitaxial thin films and devices: past, present and future
Sando, D., Barthélémy, A., & Bibès, M. (2014) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/26/47/473201 · Cited by 353
Reliability of analog resistive switching memory for neuromorphic computing
Zhao, M., Gao, B., Tang, J., Qian, H., & Wu, H. (2020) · Applied Physics Reviews · doi:10.1063/1.5124915 · Cited by 349
Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems
Li, Y., Zhong, Y., Zhang, J., Xu, L., Wang, Q., Sun, H., Tong, H., Cheng, X., & Miao, X. (2014) · Scientific Reports · doi:10.1038/srep04906 · Cited by 344
Logic Design Within Memristive Memories Using Memristor-Aided loGIC (MAGIC)
Talati, N., Gupta, S., Mane, P. S., & Kvatinsky, S. (2016) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2016.2570248 · Cited by 339
Robust resistive memory devices using solution-processable metal-coordinated azo aromatics
Goswami, S., Matula, A. J., Rath, S. P., Hedström, S., Saha, S., Annamalai, M., Sengupta, D., Patra, A., Ghosh, S., Jani, H., et al. (2017) · Nature Materials · doi:10.1038/nmat5009 · Cited by 334
Artificial Optoelectronic Synapses Based on Ferroelectric Field-Effect Enabled 2D Transition Metal Dichalcogenide Memristive Transistors
Luo, Z., Xia, X., Yang, M., Wilson, N. R., Gruverman, A. L., & Alexe, M. (2019) · ACS Nano · doi:10.1021/acsnano.9b07687 · Cited by 339
Optically Stimulated Artificial Synapse Based on Layered Black Phosphorus
Ahmed, T., Kuriakose, S., Mayes, E. L. H., Ramanathan, R., Bansal, V., Bhaskaran, M. C., Sriram, S., & Walia, S. (2019) · Small · doi:10.1002/smll.201900966 · Cited by 335
Memristors—From In‐Memory Computing, Deep Learning Acceleration, and Spiking Neural Networks to the Future of Neuromorphic and Bio‐Inspired Computing
Mehonić, A., Sebastian, A., Rajendran, B., Simeone, O., Vasilaki, E., & Kenyon, A. J. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000085 · Cited by 346
Recent developments in 2D transition metal dichalcogenides: phase transition and applications of the (quasi-)metallic phases
Yin, X., Tang, C. S., Zheng, Y., Gao, J. L., Wu, J., Zhang, H., Chhowalla, M., Chen, W., & Wee, A. T. S. (2021) · Chemical Society Reviews · doi:10.1039/d1cs00236h · Cited by 333
Engineering incremental resistive switching in TaO x based memristors for brain-inspired computing
Wang, Z., Yin, M., Zhang, T., Cai, Y., Wang, Y., Yang, Y., & Huang, R. (2016) · Nanoscale · doi:10.1039/c6nr00476h · Cited by 330
Hybrid 2D–CMOS microchips for memristive applications
Zhu, K., Pazos, S. M., Aguirre, F. L., Shen, Y., Yuan, Y. J., Zheng, W., Alharbi, O., Villena, M. A., Fang, B., Li, X., et al. (2023) · Nature · doi:10.1038/s41586-023-05973-1 · Cited by 328
Direct Observation of Conversion Between Threshold Switching and Memory Switching Induced by Conductive Filament Morphology
Sun, H., Liu, Q., Li, C., Long, S., Lv, H., Bi, C., Huo, Z., Li, L., & Liu, M. (2014) · Advanced Functional Materials · doi:10.1002/adfm.201401304 · Cited by 328
Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing
Sharbati, M. T., Du, Y., Torres, J., Ardolino, N. D., Yun, M., & Xiong, F. (2018) · Advanced Materials · doi:10.1002/adma.201802353 · Cited by 322
Nonvolatile Memories Based on Graphene and Related 2D Materials
Bertolazzi, S., Bondavalli, P., Roche, S., San, T., Choi, S., Colombo, L., Bonaccorso, F., & Samorı́, P. (2019) · Advanced Materials · doi:10.1002/adma.201806663 · Cited by 324
Towards Oxide Electronics: a Roadmap
Coll, M., Fontcuberta, J., Althammer, M., Bibès, M., Boschker, H., Calleja, A., Cheng, G., Cuoco, M., Dittmann, R., Dkhil, B., et al. (2019) · Applied Surface Science · doi:10.1016/j.apsusc.2019.03.312 · Cited by 321
“Another degree of freedom is the ferroelectric domain structure of the tunnel barrier that can be exploited for analog devices such as memristors [322].”
Background
Electronic Devices Using Open Framework Materials
Allendorf, M. D., Dong⧫, R., Feng, X., Kaskel, S., Matoga, D., & Stavila, V. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00033 · Cited by 318
Crossbar RRAM Arrays: Selector Device Requirements During Read Operation
Zhou, J., Kim, K.-H., & Lu, W. (2014) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2014.2310200 · Cited by 310
Sub-nanosecond memristor based on ferroelectric tunnel junction
Ma, C., Luo, Z. L., Huang, W., Zhao, L., Chen, Q., Lin, Y., Liu, X., Chen, Z., Liu, C., Sun, H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15249-1 · Cited by 318
Multibit memory operation of metal-oxide bi-layer memristors
Stathopoulos, S., Khiat, A., Trapatseli, M., Cortese, S., Serb, A., Valov, I., & Prodromakis, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-17785-1 · Cited by 312

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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