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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Volos, C., Pham, V., Nistazakis, H., & Stouboulos, I. (2020) · International Journal of Bifurcation and Chaos in Applied Sciences and Engineering · doi:10.1142/s0218127420300360 · Cited by 28
Artificial synapse characteristics of a ZnO-based memristor with a short-term memory effect
Yun, S., Mahata, C., Kim, M., & Kim, S. (2021) · Applied Surface Science · doi:10.1016/j.apsusc.2021.152164 · Cited by 44
MemFHE: End-to-end Computing with Fully Homomorphic Encryption in Memory
Gupta, S., Cammarota, R., & Simunic, T. (2022) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3569955 · Cited by 45
“The parameters of the model have been set to mimic the behavior of practical RRAM memory chips [53].”
Method
The rise of memtransistors for neuromorphic hardware and In-memory computing
Bae, J., Won, J., & Shim, W. (2024) · Nano Energy · doi:10.1016/j.nanoen.2024.109646 · Cited by 45
Fast and robust analog in-memory deep neural network training
Rasch, M. J., Carta, F., Fagbohungbe, O. I., & Gokmen, T. (2024) · Nature Communications · doi:10.1038/s41467-024-51221-z · Cited by 50
“In-memory computing with resistive crossbar arrays could greatly accelerate deep-learning workloads, since energy efficient acceleration can be achieved by implementing ubiquitous matrix-vector multiplications (MVMs) using resistive elements and fundamental physics (Kirchhoff’s and Ohm’s laws) (Sebastian et al., 2020;…”
Impact of program/erase operation on the performances of oxide-based resistive switching memory
Wang, G., Long, S., Yu, Z., Zhang, M., Li, Y., Xu, D., Lv, H., Liu, Q., Yan, X., Wang, M., et al. (2015) · Nanoscale Research Letters · doi:10.1186/s11671-014-0721-2 · Cited by 45
The role of ion transport phenomena in memristive double barrier devices
Dirkmann, S., Hansen, M., Ziegler, M., Kohlstedt, H., & Mussenbrock, T. (2016) · Scientific Reports · doi:10.1038/srep35686 · Cited by 45
Tailoring resistive switching in Pt/SrTiO3 junctions by stoichiometry control
Mikheev, E., Hwang, J., Kajdos, A. P., Hauser, A. J., & Stemmer, S. (2015) · Scientific Reports · doi:10.1038/srep11079 · Cited by 45
Area efficient implementation of ripple carry adder using memristor crossbar arrays
Thangkhiew, P. L., Gharpinde, R., Chowdhary, P. V., Datta, K., & Sengupta, I. (2016) · International Design and Test Workshop (IDT) · doi:10.1109/idt.2016.7843030 · Cited by 45
Research Update: Fast and tunable nanoionics in vertically aligned nanostructured films
Lee, S., & MacManus‐Driscoll, J. L. (2017) · APL Materials · doi:10.1063/1.4978550 · Cited by 45
Memristive devices for computation-in-memory
Yu, J., Nguyen, H. A. D., Xie, L., Taouil, M., & Hamdioui, S. (2018) · Design, Automation, and Test in Europe · doi:10.23919/date.2018.8342278 · Cited by 45
Tunable flexible artificial synapses: a new path toward a wearable electronic system
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Reliable Multivalued Conductance States in TaO x Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis
Lee, M., Park, G., Seo, D. H., Kwon, S. M., Lee, H., Kim, J.-S., Jung, M., You, C., Lee, H., Kim, H., et al. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b09046 · Cited by 45
SiO 2 /Ta 2 O 5 heterojunction ECM memristors: physical nature of their low voltage operation with high stability and uniformity
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SIXOR: Single-Cycle In-Memristor XOR
TaheriNejad, N. (2021) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2021.3062293 · Cited by 45
“Memristors are nonvolatile devices, often built in large arrays in a very compact fashion [3].”
Background
Insights from Adsorption and Electron Modification Studies of Polyoxometalates on Surfaces for Molecular Memory Applications
Moors, M., Warneke, J., López, X., de Graaf, C., Abel, B., & Monakhov, K. Y. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00311 · Cited by 45
Ultrafast and stable phase transition realized in MoTe 2 -based memristive devices
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Surface-Dominated HfO2 Nanorod-Based Memristor Exhibiting Highly Linear and Symmetrical Conductance Modulation for High-Precision Neuromorphic Computing
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Short-term memory characteristics in n-type-ZnO/p-type-NiO heterojunction synaptic devices for reservoir computing
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Gel-Based Ionic Circuits
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Compact Ga-Doped ZnO Nanorod Thin Film for Making High-Performance Transparent Resistive Switching Memory
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A multilevel memristor–CMOS memory cell as a ReRAM
Rabbani, P., Dehghani, R., & Shahpari, N. (2015) · Microelectronics Journal · doi:10.1016/j.mejo.2015.10.006 · Cited by 45
A hybrid memristor-CMOS XOR gate for nonvolatile logic computation
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Ternary Synaptic Plasticity Arising from Memdiode Behavior of TiOx Single Nanowires
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A Mapping Methodology of Boolean Logic Circuits on Memristor Crossbar
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Effect of top electrode material on resistive switching properties of WN based thin films for non volatile memory application
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Mott insulators: A large class of materials for Leaky Integrate and Fire (LIF) artificial neuron
Adda, C., Corraze, B., Stoliar, P., Diener, P., Tranchant, J., Filatre‐Furcate, A., Fourmigué, M., Lorcy, D., Besland, M., Janod, É., et al. (2018) · Journal of Applied Physics · doi:10.1063/1.5042756 · Cited by 44

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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