Atomic Nb-doping of WS2 for high-performance synaptic transistors in neuromorphic computing

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS<sub>2</sub> and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 10<sup>3</sup>, 100 times larger than that of devices prepared with undoped WS<sub>2</sub>. The Nb atoms in WS<sub>2</sub> play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS<sub>2</sub> synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study's contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.

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Publication Details

Journal
Microsystems & Nanoengineering
Published
2024-09-26
DOI
https://doi.org/10.1038/s41378-024-00779-1
Citations
17
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.55

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article

Atomic Nb-doping of WS2 for high-performance synaptic transistors in neuromorphic computing

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17 citations
Microsystems & Nanoengineering
Advanced Memory and Neural Computing
3.55
article

Atomic Nb-doping of WS2 for high-performance synaptic transistors in neuromorphic computing

Simin Feng, Kejie Guan, Yinxiao Li, Fuqin Sun, Weifan Zhou, Zhengyang Cai, Cheng Zhang, Zhuo Zheng, Yingyi Wang, Lin Liu, Ting Zhang, Xiaowei Wang
article en
17 citations

Abstract

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS<sub>2</sub> and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 10<sup>3</sup>, 100 times larger than that of devices prepared with undoped WS<sub>2</sub>. The Nb atoms in WS<sub>2</sub> play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS<sub>2</sub> synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study's contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.

Microsystems & NanoengineeringVol. 10(1)
Jiangnan University (CN), University of Science and Technology of China (CN), Suzhou University of Science and Technology (CN), Nanjing University of Science and Technology (CN), Suzhou Institute of Nano-tech and Nano-bionics (CN), Xi’an Jiaotong-Liverpool University (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, China Postdoctoral Science Foundation, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Suzhou Institute of Nanotechnology, Chinese Academy of Sciences, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.55
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