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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The missing memristor found
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Self-citation
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 1×
“(1) Schottky emission: thermally activated electrons injected over the barrier into”
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
Waser, R., Dittmann, R., Staikov, G., & Szot, K. (2009) · Advanced Materials · doi:10.1002/adma.200900375 · Cited by 5,080
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Phase-change materials for rewriteable data storage
Wuttig, M., & Yamada, N. (2007) · Nature Materials · doi:10.1038/nmat2009 · Cited by 3,664
Giant tunnelling magnetoresistance at room temperature with MgO (100) tunnel barriers
Parkin, S., Kaiser, C., Panchula, A. F., Rice, P. M., Hughes, B. P., Samant, M. G., & Yang, S. (2004) · Nature Materials · doi:10.1038/nmat1256 · Cited by 3,327
Memristive switching mechanism for metal/oxide/metal nanodevices
Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987
Self-citation
Resistive switching in transition metal oxides
Sawa, A. (2008) · Materials Today · doi:10.1016/s1369-7021(08)70119-6 · Cited by 2,949
Metal–Oxide RRAM
Wong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., Lee, B., Chen, F. T., & Tsai, M.-J. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2190369 · Cited by 2,757

Show all 168 references

Research works printed but not linked: 3

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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Yan, K., Chen, B., Hu, H., Chen, S., Dong, B., Gao, X., Xiao, X., Zhou, J., & Zou, D. (2016) · Advanced Electronic Materials · doi:10.1002/aelm.201600160 · Cited by 68
High-Performance TiN/Al2O3/ZnO/Al2O3/TiN Flexible RRAM Device With High Bending Condition
Kumar, D., Chand, U., Lew, W. S., & Tseng, T. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2019.2959883 · Cited by 66
“6(a) and (d) shows the schematics of the formation and rupture of CFs for the ZnO-based device.”
BackgroundInfluential
Mechanistic and Kinetic Analysis of Perovskite Memristors with Buffer Layers: The Case of a Two-Step Set Process
Gonzales, C., & Guerrero, A. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c03669 · Cited by 67
Thickness effect of ultra-thin Ta2O5 resistance switching layer in 28 nm-diameter memory cell
Park, T. H., Song, S. J., Kim, H. J., Kim, S. G., Chung, S., Kim, B. Y., Lee, K. J., Kim, K. M., Choi, B. J., & Hwang, C. S. (2015) · Scientific Reports · doi:10.1038/srep15965 · Cited by 67
Nonvolatile reconfigurable sequential logic in a HfO 2 resistive random access memory array
Zhou, Y., Li, Y., Su, Y., Wang, Z., Shih, L.-Y., Chang, T., Chang, K., Long, S., Sze, S. M., & Miao, X. (2017) · Nanoscale · doi:10.1039/c7nr00934h · Cited by 67
Mellow writes
Zhang, L., Neely, B., Franklin, D. M., Strukov, D. B., Xie, Y., & Chong, F. T. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001192 · Cited by 66
Self-citation
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Thangkhiew, P. L., Gharpinde, R., & Datta, K. (2018) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2018.2792474 · Cited by 66
TIME: A Training-in-Memory Architecture for RRAM-Based Deep Neural Networks
Cheng, M., Xia, L., Zhu, Z., Cai, Y., Xie, Y., Wang, Y., & Yang, H. (2018) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2018.2824304 · Cited by 67
“1) Endurance: According to [28] and [29], the endurance of RRAM is 1012 at most while the endurance of DRAM and the endurance of SRAM is up to 1016 [30].”
Background
Synaptic Plasticity and Filtering Emulated in Metal–Organic Frameworks Nanosheets Based Transistors
Ding, G., Yang, B., Zhou, K., Zhang, C., Wang, Y., Yang, J., Han, S., Zhai, Y., Roy, V. A. L., & Zhou, Y. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900978 · Cited by 64
Self-citation
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Ranganathan, K., Fiegenbaum‐Raz, M., & Ismach, A. F. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202005718 · Cited by 65
Advances in Emerging Photonic Memristive and Memristive‐Like Devices
Wang, W., Gao, S., Wang, Y., Li, Y., Yue, W., Niu, H., Yin, F., Guo, Y., & Shen, G. (2022) · Advanced Science · doi:10.1002/advs.202105577 · Cited by 68
Emerging higher-order memristors for bio-realistic neuromorphic computing: A review
Chaurasiya, R., Shih, L., Chen, K., & Chen, J. (2023) · Materials Today · doi:10.1016/j.mattod.2023.08.002 · Cited by 66
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Ramı́rez, P., Gómez, V., Cervera, J., Mafé, S., & Bisquert, J. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c02796 · Cited by 66
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Gaba, S., Knag, P. C., Zhang, Z., & Lu, W. D. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865703 · Cited by 66
Self-Selecting Resistive Switching Scheme Using TiO2 Nanorod Arrays
Huang, C., Chou, T., Huang, J., Lin, S.-M., & Chueh, Y. (2017) · Scientific Reports · doi:10.1038/s41598-017-01354-7 · Cited by 66
Variability Improvement of TiO x /Al2O3 Bilayer Nonvolatile Resistive Switching Devices by Interfacial Band Engineering with an Ultrathin Al2O3 Dielectric Material
Banerjee, W., Xu, X., Lv, H., Liu, Q., Long, S., & Liu, M. (2017) · ACS Omega · doi:10.1021/acsomega.7b01211 · Cited by 66
Memristors for the Curious Outsiders
Carbajal, J. P., & Caravelli, F. (2018) · Technologies · doi:10.3390/technologies6040118 · Cited by 70
Annealing atmosphere effect on the resistive switching and magnetic properties of spinel Co 3 O 4 thin films prepared by a sol–gel technique
Yao, C., Ismail, M., Hao, A., Thatikonda, S. K., Huang, W., Qin, N., & Bao, D. (2019) · RSC Advances · doi:10.1039/c9ra01121h · Cited by 66
Resistance switching characteristics and mechanisms of MXene/SiO2 structure-based memristor
Lian, X., Shen, X., Zhang, M., Xu, J., Gao, F., Wan, X., Hu, E., Guo, Y., Zhao, J. H., & Tong, Y. (2019) · Applied Physics Letters · doi:10.1063/1.5087423 · Cited by 66
Lithium‐Battery Anode Gains Additional Functionality for Neuromorphic Computing through Metal–Insulator Phase Separation
Gonzalez‐Rosillo, J. C., Balaish, M., Hood, Z. D., Nadkarni, N., Fraggedakis, D., Kim, K. J., Mullin, K. M., Pfenninger, R., Bazant, M. Z., & Rupp, J. L. M. (2020) · Advanced Materials · doi:10.1002/adma.201907465 · Cited by 66
Fast Magneto-Ionic Switching of Interface Anisotropy Using Yttria-Stabilized Zirconia Gate Oxide
Lee, K., Jo, S., Tan, A. J., Huang, M., Choi, D., Park, J. H., Ji, H., Son, J., Chang, J., Beach, G. S. D., et al. (2020) · Nano Letters · doi:10.1021/acs.nanolett.0c00340 · Cited by 66
Piezotronic neuromorphic devices: principle, manufacture, and applications
Lin, X., Feng, Z., Xiong, Y., Sun, W., Yao, W., Wei, Y., Wang, Z. L., & Sun, Q. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad339b · Cited by 67
Conformal Neuromorphic Bioelectronics for Sense Digitalization
Zhao, X. Q., Zou, H., Wang, M., Wang, J., Wang, T., Wang, L., & Chen, X. (2024) · Advanced Materials · doi:10.1002/adma.202403444 · Cited by 68
Multi-scale quantum point contact model for filamentary conduction in resistive random access memories devices
Lian, X., Cartoixà, X., Miranda, E. A., Perniola, L., Rurali, R., Long, S., Liu, M., & Suñé, J. (2014) · Journal of Applied Physics · doi:10.1063/1.4885419 · Cited by 66
A high performance transparent resistive switching memory made from ZrO2/AlON bilayer structure
Tsai, T.-L., Chang, H.-Y., Lou, J. J.-C., & Tseng, T. (2016) · Applied Physics Letters · doi:10.1063/1.4946006 · Cited by 65

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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