The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor
Abstract
A single synaptic device with inherent learning and memory functions is demonstrated based on an amorphous InGaZnO (α‐IGZO) memristor; several essential synaptic functions are simultaneously achieved in such a single device, including nonlinear transmission characteristics, spike‐rate‐dependent and spike‐timing‐dependent plasticity, long‐term/short‐term plasticity (LSP and STP) and “learning‐experience” behavior. These characteristics bear striking resemblances to certain learning and memory functions of biological systems. Especially, a “learning‐experience” function is obtained for the first time, which is thought to be related to the metastable local structures in α‐IGZO. These functions are interrelated: frequent stimulation can cause an enhancement of LTP, both spike‐rate‐dependent and spike‐timing‐dependent plasticity is the same on this point; and, the STP‐to‐LTP transition can occur through repeated “stimulation” training. The physical mechanism of device operation, which does not strictly follow the memristor model, is attributed to oxygen ion migration/diffusion. A correlation between short‐term memory and ion diffusion is established by studying the temperature dependence of the relaxation processes of STP and ion diffusion. The realization of important synaptic functions and the establishment of a dynamic model would promote more accurate modeling of the synapse for artificial neural network.
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Cites
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Atomic structure of conducting nanofilaments in TiO2 resistive switching memory
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Self-citationShort-term plasticity and long-term potentiation mimicked in single inorganic synapses
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Cited by
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
Self-citationMemristive 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
Artificial synapse network on inorganic proton conductor for neuromorphic systems
Zhu, L. Q., Wan, C. J., Guo, L. Q., Shi, Y., & Wan, Q. (2014) · Nature Communications · doi:10.1038/ncomms4158 · Cited by 849
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
Kim, S., Du, C., Sheridan, P., Ma, W., Choi, S., & Lu, W. D. (2015) · Nano Letters · doi:10.1021/acs.nanolett.5b00697 · Cited by 603
Metadata
- Authors
- Wang, Z. Q., Xu, H. Y., Li, X. H., Yu, H., Liu, Y. C., & Zhu, X. J.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2012-04-10
- Volume
- 22
- Issue
- 13
- Pages
- 2759-2765
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W2162341456
- Cited by
- 815
- References
- 11
- FWCI
- 15.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, Z. Q., Xu, H. Y., Li, X. H., Yu, H., Liu, Y. C., & Zhu, X. J. (2012). Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor. Advanced Functional Materials. https://doi.org/10.1002/adfm.201103148
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