The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Learning through ferroelectric domain dynamics in solid-state synapses
Open accessAbstract
In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses and set to evolve according to a biological learning rule called spike-timing-dependent plasticity (STDP). Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Here we report on synapses based on ferroelectric tunnel junctions and show that STDP can be harnessed from inhomogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, we demonstrate that conductance variations can be modelled by the nucleation-dominated reversal of domains. Based on this physical model, our simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning in spiking neural networks.
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Cites
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Cited by
Resistive 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
Two-dimensional materials for next-generation computing technologies
Liu, C., Chen, H., Wang, S., Liu, Q., Jiang, Y.-G., Zhang, D. W., Liu, M., & Zhou, P. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0724-3 · Cited by 1,045
Neuromorphic computing with multi-memristive synapses
Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 899
A comprehensive review on emerging artificial neuromorphic devices
Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 758
Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics
Park, H., Lee, Y., Kim, N., Seo, D., Go, G., & Lee, T. (2020) · Advanced Materials · doi:10.1002/adma.201903558 · Cited by 533
Metadata
- Authors
- Boyn, S., Grollier, J., Lecerf, G., Xu, B., Locatelli, N., Fusil, S., Girod, S., Carrétéro, C., Garcia, K., Xavier, S., Tomas, J., Bellaiche, L., Bibes, M., Barthélémy, A., Saïghi, S., & Garcia, V.
- Venue
- Nature Communications
- Published
- 2017-04-03
- Volume
- 8
- Issue
- 1
- Pages
- 14736-14736
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2604443664
- Cited by
- 600
- References
- 32
- FWCI
- 23.16
- Type
- journal article
- Language
- en
- Open access
- gold
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- Boyn, S., Grollier, J., Lecerf, G., Xu, B., Locatelli, N., Fusil, S., Girod, S., Carrétéro, C., Garcia, K., Xavier, S., Tomas, J., Bellaiche, L., Bibes, M., Barthélémy, A., Saïghi, S., & Garcia, V. (2017). Learning through ferroelectric domain dynamics in solid-state synapses. Nature Communications. https://doi.org/10.1038/ncomms14736
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