Nitrogen-doped carbon quantum dot-decorated In2O3 synaptic transistors for neuromorphic computing

Nitrogen-doped carbon quantum dots (N-CQDs) are promising materials for electronic devices due to their variable bandgap and structural stability. Here, we integrate N-CQDs into In2O3 synaptic transistors with electrolyte gating, resulting in a hybrid structure. The surface functional groups and defects of N-CQDs empower the charge trapping mechanism, permitting controlled conduction and charge regulation, which are crucial for emulating linear and symmetric artificial synaptic devices. Devices incorporating N-CQDs demonstrate enhanced stability and memory characteristics, low energy consumption, consistent retention, and a significant hysteresis window across multiple voltage cycles. Finally, the study emulates biological synapses and cognitive functions, achieving an energy consumption of 10 fJ per synaptic event and a pattern recognition accuracy of 91.2% on the MNIST dataset in hardware neural networks. This work demonstrates the potential of well-manipulating charge trapping in N-CQDs to develop high-performance, nonvolatile synaptic devices.

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Publication Details

Journal
Applied Physics Letters
Published
2024-11-04
DOI
https://doi.org/10.1063/5.0231655
Citations
13
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.71

Funders

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Nitrogen-doped carbon quantum dot-decorated In2O3 synaptic transistors for neuromorphic computing

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13 citations
Applied Physics Letters
Advanced Memory and Neural Computing
2.71
article

Nitrogen-doped carbon quantum dot-decorated In2O3 synaptic transistors for neuromorphic computing

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article en
13 citations

Abstract

Nitrogen-doped carbon quantum dots (N-CQDs) are promising materials for electronic devices due to their variable bandgap and structural stability. Here, we integrate N-CQDs into In2O3 synaptic transistors with electrolyte gating, resulting in a hybrid structure. The surface functional groups and defects of N-CQDs empower the charge trapping mechanism, permitting controlled conduction and charge regulation, which are crucial for emulating linear and symmetric artificial synaptic devices. Devices incorporating N-CQDs demonstrate enhanced stability and memory characteristics, low energy consumption, consistent retention, and a significant hysteresis window across multiple voltage cycles. Finally, the study emulates biological synapses and cognitive functions, achieving an energy consumption of 10 fJ per synaptic event and a pattern recognition accuracy of 91.2% on the MNIST dataset in hardware neural networks. This work demonstrates the potential of well-manipulating charge trapping in N-CQDs to develop high-performance, nonvolatile synaptic devices.

Applied Physics LettersVol. 125(19)
Central South University (CN), Hunan University (CN), State Key Laboratory of Transducer Technology (CN), Shanghai Institute of Microsystem and Information Technology (CN), South University (US)
National Natural Science Foundation of China, National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 9%
Advanced Memory and Neural Computing
2.71
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