The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Sub-10 nm Ta Channel Responsible for Superior Performance of a HfO2 Memristor
Open accessAbstract
Memristive devices are promising candidates for the next generation non-volatile memory and neuromorphic computing. It has been widely accepted that the motion of oxygen anions leads to the resistance changes for valence-change-memory (VCM) type of materials. Only very recently it was speculated that metal cations could also play an important role, but no direct physical characterizations have been reported yet. Here we report a Ta/HfO 2/Pt memristor with fast switching speed, record high endurance (120 billion cycles) and reliable retention. We programmed the device to 24 discrete resistance levels, and also demonstrated over a million (2 20) epochs of potentiation and depression, suggesting that our devices can be used for both multi-level non-volatile memory and neuromorphic computing applications. More importantly, we directly observed a sub-10 nm Ta-rich and O-deficient conduction channel within the HfO 2 layer that is responsible for the switching. This work deepens our understanding of the resistance switching mechanism behind oxide-based memristive devices and paves the way for further device performance optimization for a broad spectrum of applications.
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Cites
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268
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
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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Memristive switching mechanism for metal/oxide/metal nanodevices
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“As such, the switching cannot be attributed to the modulation of tunneling gap size as found in TiO x based devices 30,31 .”
Self-citationLee, M.-J., Lee, C. B., Lee, D., Lee, S. R., Chang, M., Hur, J. H., Kim, Y.-B., Kim, C.-J., Seo, D. H., Seo, S., et al. (2011) · Nature Materials · doi:10.1038/nmat3070 · Cited by 2,228
Atomic structure of conducting nanofilaments in TiO2 resistive switching memory
Kwon, D.-H., Kim, K. M., Jang, J. H., Jeon, J. M., Lee, M. H., Kim, G. H., Li, X.-S., Park, G.-S., Lee, B., Han, S., et al. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2009.456 · Cited by 2,092
Synaptic plasticity: LTP and LTD
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Observation of conducting filament growth in nanoscale resistive memories
Yang, Y., Gao, P., Gaba, S., Chang, T., Pan, X., & Lu, W. (2012) · Nature Communications · doi:10.1038/ncomms1737 · Cited by 1,208 · cited 1×
“For electrochemical metallization memory (ECM) systems 12 , it has been observed that formation and dissolution of metallic filaments (e.g., Cu or Ag) are responsible for the low and high resistance states 13,14 .”
Yang, Y. C., Pan, F., Liu, Q., Liu, M., & Zeng, F. (2009) · Nano Letters · doi:10.1021/nl900006g · Cited by 876
Sub-nanosecond switching of a tantalum oxide memristor
Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G., & Williams, R. S. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/48/485203 · Cited by 701
Memristor−CMOS Hybrid Integrated Circuits for Reconfigurable Logic
Xia, Q., Robinett, W., Cumbie, M. W., Banerjee, N., Cardinali, T. J., Yang, J. J., Wu, W., Li, X., Tong, W. M., Strukov, D. B., et al. (2009) · Nano Letters · doi:10.1021/nl901874j · Cited by 671
Self-citationPickett, M. D., & Stanley Williams, R. (2012) · Nanotechnology · doi:10.1088/0957-4484/23/21/215202 · Cited by 385
Cited by
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-citationAnalogue signal and image processing with large memristor crossbars
Li, C., Hu, M., Li, Y., Jiang, H., Ge, N., Montgomery, E., Zhang, J., Song, W., Dávila, N., Graves, C. E., et al. (2017) · Nature Electronics · doi:10.1038/s41928-017-0002-z · Cited by 1,292
Self-citationResistive switching materials for information processing
Wang, Z., Wu, H., Burr, G. W., Hwang, C. S., Wang, K. L., Xia, Q., & Yang, J. J. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0159-3 · Cited by 1,214
Self-citationEfficient and self-adaptive in-situ learning in multilayer memristor neural networks
Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., Jiang, H., Montgomery, E., Lin, P., Wang, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04484-2 · Cited by 931
Self-citationMemristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine
Hu, M., Graves, C. E., Li, C., Li, Y., Ge, N., Montgomery, E., Davila, N., Jiang, H., Williams, R. S., Yang, J. J., et al. (2018) · Advanced Materials · doi:10.1002/adma.201705914 · Cited by 751
Self-citation
Metadata
- Authors
- Jiang, H., Han, L., Lin, P., Wang, Z., Jang, M. H., Wu, Q., Barnell, M., Yang, J. J., Xin, H. L., & Xia, Q.
- Venue
- Scientific Reports
- Published
- 2016-06-23
- Volume
- 6
- Issue
- 1
- Pages
- 28525-28525
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2468323742
- Cited by
- 277
- References
- 13
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- gold
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