The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Energy‐Efficient Hybrid Perovskite Memristors and Synaptic Devices
Abstract
New parallel computing architectures based on neuromorphic computing are needed due to their advantages over conventional computation with regards to real‐time processing of unstructured sensory data such as image, video, or voice. However, developing artificial neuromorphic system remains a challenge due to the lack of electronic synaptic devices, which can mimic all the functions of biological synapses with low energy consumption. Here it is reported that two‐terminal organometal trihalide perovskite (OTP) synaptic devices can mimic the neuromorphic learning and remembering process. Various functions known in biological synapses are demonstrated in OTP synaptic devices including four forms of spike‐timing‐dependent plasticity (STDP), spike‐rate‐dependent plasticity (SRDP), short‐term plasticity (STP) and long‐term potentiation (LTP)), and learning‐experience behavior. The excellent photovoltaic property of the OTP devices also enables photo‐read synaptic functions. The perovskite synapse has the potential of low energy consumption of femto‐Joule/(100 nm)2 per event, which is close to the energy consumption of biological synapses. The demonstration of energy‐efficient OTP synaptic devices opens a new plausible application of OTP materials into neuromorphic devices, which offer the high connectivity and high density required for biomimic computing.
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Calado, P., Telford, A. M., Bryant, D., Li, X., Nelson, J., O’Regan, B. C., & Barnes, P. R. F. (2016) · Nature Communications · doi:10.1038/ncomms13831 · Cited by 845
Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 834
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
Tian, H., Zhao, L., Wang, X., Yeh, Y.-W., Yao, N., Rand, B. P., & Ren, T.-L. (2017) · ACS Nano · doi:10.1021/acsnano.7b05726 · Cited by 371
Metadata
- Authors
- Xiao, Z., & Huang, J.
- Venue
- Advanced Electronic Materials
- Publisher
- Wiley
- Published
- 2016-05-03
- Volume
- 2
- Issue
- 7
- ISSN
- 2199-160X (ISSN-L)
- OpenAlex
- W2345421161
- Cited by
- 413
- References
- 9
- FWCI
- 14.79
- Type
- journal article
- Language
- en
- Open access
- closed
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