The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing
Abstract
Abstract Optically stimulated synaptic devices are critical to the development of neuromorphic computing with broad bandwidth and efficient interconnect. Although a few interesting materials have been employed to fabricate optically stimulated synaptic devices, the use of silicon (Si) that is the material of choice for very large-scale integration circuits in the conventional von Neumann computing has not been explored for optically stimulated synaptic devices. Here we take advantage of one of the most important nanostructures of Si — Si nanocrystals (NCs) to make synaptic devices, which can be effectively stimulated by light in the unprecedented broad spectral region from the ultraviolet to near-infrared, approaching the wavelength of ∼ 2 µm. These optically stimulated Si-NC-based synaptic devices demonstrate a series of important synaptic functionalities, well mimicking biological synapses. The plasticity of Si-NC-based synaptic devices originates from the dynamic trapping and release of photogenerated carriers at defects such as dangling bonds at the NC surface. The current facile use of Si NCs in broadband optoelectronic synaptic devices with low energy consumption has important implication for the large-scale deployment of Si in the emerging neuromorphic computing.
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Cites
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
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Cited by
Pham, P. V., Bodepudi, S. C., Shehzad, K., Liu, Y., Xu, Y., Yu, B., & Duan, X. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00735 · Cited by 708
Self-citationAn Oxide Schottky Junction Artificial Optoelectronic Synapse
Gao, S., Liu, G., Yang, H., Hu, C., Chen, Q., Gong, G., Xue, W., Yi, X., Shang, J., & Li, R.-W. (2019) · ACS Nano · doi:10.1021/acsnano.9b00340 · Cited by 494
Wang, M., Luo, Y., Wang, T., Wan, C., Pan, L., Pan, S., He, K., Neo, A., & Chen, X. (2021) · Advanced Materials · doi:10.1002/adma.202003014 · Cited by 451
Bioinspired mechano-photonic artificial synapse based on graphene/MoS 2 heterostructure
Yu, J., Yang, X., Gao, G., Xiong, Y., Wang, Y., Han, J., Chen, Y., Zhang, H., Sun, Q., & Wang, Z. L. (2021) · Science Advances · doi:10.1126/sciadv.abd9117 · Cited by 389
Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing
John, R. A., Demirağ, Y., Shynkarenko, Y., Berezovska, Y., Ohannessian, N., Payvand, M., Zeng, P., Bodnarchuk, M. I., Krumeich, F., Kara, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29727-1 · Cited by 292
Metadata
- Authors
- Tan, H., Ni, Z., Peng, W., Du, S., Liu, X., Zhao, S., Li, W., Ye, Z., Xu, M., Xu, Y., Pi, X., & Yang, D.
- Venue
- Nano Energy
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 52
- Pages
- 422-430
- ISSN
- 2211-2855 (ISSN-L)
- OpenAlex
- W2886681379
- Cited by
- 234
- References
- 10
- FWCI
- 7.81
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tan, H., Ni, Z., Peng, W., Du, S., Liu, X., Zhao, S., Li, W., Ye, Z., Xu, M., Xu, Y., Pi, X., & Yang, D. (2018). Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy. https://doi.org/10.1016/j.nanoen.2018.08.018
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