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Nano Research 2024, 17(5): 4622-4630 https://doi.org/10.1007/s12274-023-6382-7
Research Article | Issue | Published: 29 December 2023

Salt-assisted vapor–liquid–solid growth of high-quality ultrathin nickel oxide flakes for artificial synapses in image recognition applications

Show Author's Information Hui Zhang1,2,§,  Zongjie Shen2,§,  Alei Li2,  Lin Wang2,  Qinan Wang2,  Yunfei Li2,  Yunlei Zhong2,  Juntong Zhu3,  Yong Zhang2,  Mengjiao Han4,  Dan Tian6,  Chun Zhao5( ),  Lixing Kang2( ),  Qingwen Li1,2( )
School of Physical Science and Technology, Shanghai Tech University, Shanghai 201210, China
Division of Advanced Materials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
College of Energy, Soochow Institute for Energy and Materials Innovations, and Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, Soochow University, Suzhou 215123, China
Songshan Lake Materials Laboratory, Dongguan 523808, China
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
College of Materials Science and Engineering, Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China

§ Hui Zhang and Zongjie Shen contributed equally to this work.

Keywords:
memristor, synapse, vapor–liquid–solid (VLS) growth, generative adversarial network (GAN), nickel oxide (NiO) flakes
Topics:
Film growthNickel oxide
Cite this article:
Zhang H, Shen Z, Li A, et al. Salt-assisted vapor–liquid–solid growth of high-quality ultrathin nickel oxide flakes for artificial synapses in image recognition applications. Nano Research, 2024, 17(5): 4622-4630. https://doi.org/10.1007/s12274-023-6382-7
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Abstract Full text Electronic supplementary material About this article

Transition metal oxides have attracted intense interest owing to their abundant physical and chemical properties. The controlled preparation of large-area, high-quality two-dimensional crystals is essential for revealing their inherent properties and realizing high-performance devices. However, fabricating two-dimensional (2D) transition metal oxides using a general approach still presents substantial challenges. Herein, we successfully achieve highly crystalline nickel oxide (NiO) flakes with a thickness as thin as 3.3 nm through the salt-assisted vapor–liquid–solid (VLS) growth method, which demonstrated exceptional stability under ambient conditions. To explore the great potential of the NiO crystal in this work, an artificial synapse based on the NiO-flake resistive switching (RS) layer is investigated. Short-term and long-term synaptic behaviors are obtained with external stimuli. The artificial synaptic performance provides the foundation of the neuromorphic application, including handwriting number recognition based on artificial neuron network (ANN) and the virtually unsupervised learning capability based on generative adversarial network (GAN). This pioneering work not only paves new paths for the synthesis of 2D oxides in the future but also demonstrates the substantial potential of oxides in the field of neuromorphic computing.


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Abstract
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Electronic supplementary material
About this article

Salt-assisted vapor–liquid–solid growth of high-quality ultrathin nickel oxide flakes for artificial synapses in image recognition applications

Show Author's information Hui Zhang1,2,§, Zongjie Shen2,§, Alei Li2, Lin Wang2, Qinan Wang2, Yunfei Li2, Yunlei Zhong2, Juntong Zhu3, Yong Zhang2, Mengjiao Han4, Dan Tian6, Chun Zhao5( ), Lixing Kang2( ), Qingwen Li1,2( )
School of Physical Science and Technology, Shanghai Tech University, Shanghai 201210, China
Division of Advanced Materials, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
College of Energy, Soochow Institute for Energy and Materials Innovations, and Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, Soochow University, Suzhou 215123, China
Songshan Lake Materials Laboratory, Dongguan 523808, China
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
College of Materials Science and Engineering, Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China

§ Hui Zhang and Zongjie Shen contributed equally to this work.

Abstract

Transition metal oxides have attracted intense interest owing to their abundant physical and chemical properties. The controlled preparation of large-area, high-quality two-dimensional crystals is essential for revealing their inherent properties and realizing high-performance devices. However, fabricating two-dimensional (2D) transition metal oxides using a general approach still presents substantial challenges. Herein, we successfully achieve highly crystalline nickel oxide (NiO) flakes with a thickness as thin as 3.3 nm through the salt-assisted vapor–liquid–solid (VLS) growth method, which demonstrated exceptional stability under ambient conditions. To explore the great potential of the NiO crystal in this work, an artificial synapse based on the NiO-flake resistive switching (RS) layer is investigated. Short-term and long-term synaptic behaviors are obtained with external stimuli. The artificial synaptic performance provides the foundation of the neuromorphic application, including handwriting number recognition based on artificial neuron network (ANN) and the virtually unsupervised learning capability based on generative adversarial network (GAN). This pioneering work not only paves new paths for the synthesis of 2D oxides in the future but also demonstrates the substantial potential of oxides in the field of neuromorphic computing.

Keywords: memristor, synapse, vapor–liquid–solid (VLS) growth, generative adversarial network (GAN), nickel oxide (NiO) flakes

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Publication history
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Acknowledgements

Publication history

Received: 20 October 2023
Revised: 21 November 2023
Accepted: 28 November 2023
Published: 29 December 2023
Issue date: May 2024

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

The authors are grateful for the photo provided by Yezhou Ni for image recognition. The authors acknowledge support from the Jiangsu Funding Program for Excellent Postdoctoral Talent, the National Natural Science Foundation of China (No. 52372055), and the Jiangsu Independent Innovation Fund Project of Agricultural Science and Technology (No. CX (21) 3163). The authors are grateful for the technical support for Nano-X from Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences (SINANO).

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