The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Engineering incremental resistive switching in TaOxbased memristors for brain-inspired computing
Abstract
Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament growth (dissolution) dynamics with time, therefore significantly improving the conductance modulation linearity that is desirable in neuromorphic systems. In addition, the introduction of the DLL also served to reduce the power consumption of the memristor, and important synaptic learning rules in biological brains such as spike timing dependent plasticity were successfully implemented using these optimized devices. This study could provide general implications for continued optimizations of memristor performance for neuromorphic applications, by carefully tuning the dynamics involved in filament growth and dissolution.
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Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
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Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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Memristive devices for computing
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Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
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Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
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Observation of conducting filament growth in nanoscale resistive memories
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Neuro-inspired computing chips
Zhang, W., Gao, B., Tang, J., Yao, P., Yu, S., Chang, M.-F., Yoo, H.-J., Qian, H., & Wu, H. (2020) · Nature Electronics · doi:10.1038/s41928-020-0435-7 · Cited by 892
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
Self-citationMemristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics
Huh, W., Lee, D., & Lee, C. (2020) · Advanced Materials · doi:10.1002/adma.202002092 · Cited by 586
Hardware implementation of memristor-based artificial neural networks
Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45670-9 · Cited by 564
Self-citationBrain-inspired computing with memristors: Challenges in devices, circuits, and systems
Zhang, Y., Wang, Z., Zhu, J., Yang, Y., Rao, M., Song, W., Zhuo, Y., Zhang, X., Cui, M., Shen, L., et al. (2020) · Applied Physics Reviews · doi:10.1063/1.5124027 · Cited by 384
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Metadata
- Authors
- Wang, Z., Yin, M., Zhang, T., Cai, Y., Wang, Y., Yang, Y., & Huang, R.
- Venue
- Nanoscale
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2016
- Volume
- 8
- Issue
- 29
- Pages
- 14015-14022
- ISSN
- 2040-3364 (ISSN-L)
- 2040-3372
- OpenAlex
- W2336860599
- Cited by
- 329
- References
- 11
- FWCI
- 13.87
- Type
- journal article
- Language
- en
- Open access
- closed
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