The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol
Abstract
Recent electronic applications require an efficient computing system that can perform data processing with limited energy consumption. Inspired by the massive parallelism of the human brain, a neuromorphic system (hardware neural network) may provide an efficient computing unit to perform such tasks as classification and recognition. However, the implementation of synaptic devices (i.e., the essential building blocks for emulating the functions of biological synapses) remains challenging due to their uncontrollable weight update protocol and corresponding uncertain effects on the operation of the system, which can lead to a bottleneck in the continuous design and optimization. Here, we demonstrate a synaptic transistor based on highly purified, preseparated 99% semiconducting carbon nanotubes, which can provide adjustable weight update linearity and variation margin. The pattern recognition efficacy is validated using a device-to-system level simulation framework. The enlarged margin rather than the linear weight update can enhance the fault tolerance of the recognition system, which improves the recognition accuracy.
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Citations
Cites
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
Abbott, L. F., & Regehr, W. G. (2004) · Nature · doi:10.1038/nature03010 · Cited by 1,684
Resistance switching memories are memristors
Chua, L. (2011) · Applied Physics A · doi:10.1007/s00339-011-6264-9 · Cited by 1,434 · cited 1×
“12 With memristors, analog conductance states can be maintained over the long-term, and only a minuscule amount of energy is required to modulate distinct states.”
BackgroundYu, S., Wu, Y., Jeyasingh, R., Kuzum, D., & Wong, H.-S. P. (2011) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2011.2147791 · Cited by 805
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
Self-citationDu, C., Ma, W., Chang, T., Sheridan, P., & Lu, W. D. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201501427 · Cited by 444
Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
Seung, H. S. (2003) · Neuron · doi:10.1016/s0896-6273(03)00761-x · Cited by 340
Cited by
Neuromorphic nanoelectronic materials
Sangwan, V. K., & Hersam, M. C. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0647-z · Cited by 890
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
The Future of Memristors: Materials Engineering and Neural Networks
Sun, K., Chen, J., & Yan, X. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202006773 · Cited by 482
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
Metadata
- Authors
- Kim, S., Choi, B., Lim, M., Yoon, J., Lee, J., Kim, H.-D., & Choi, S.-J.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-02-23
- Volume
- 11
- Issue
- 3
- Pages
- 2814-2822
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2590692683
- Cited by
- 338
- References
- 8
- FWCI
- 12.75
- Type
- journal article
- Language
- en
- Open access
- closed
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