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Artificial synapse based on Co3O4 nanosheets for high-accuracy pattern recognition |
| Ying Li(李颖)1, Xiaofan Zhou(周晓凡)1, Jiajun Guo(郭家俊)1,†, Tong Chen(陈通)2,3, Xiaohui Zhang(张晓辉)1, Xia Xiao(肖夏)1, Guangyu Wang(王光宇)1,‡, Mehran Khan Alam4, Qi Zhang(张琪)5, and Liqian Wu(武力乾)6,§ |
1 School of Physical Science and Information Technology, Liaocheng University, Liaocheng 252059, China;
2 School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China;
3 Institute of Advanced Semiconductors, Hangzhou Innovation Center, Zhejiang University, Hangzhou 311200, China;
4 Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials (Ministry of Education), School of Materials Science and Engineering, Shandong University, Jinan 250061, China;
5 School of Medical Information Engineering, Department of Physics, Jining Medical University, Jining 272067, China;
6 Wenzhou Institute of Hangzhou Dianzi University, Wenzhou 325038, China |
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Abstract Two-dimensional (2D) metal oxides are promising candidates for constructing neuromorphic systems because of their intriguing physical properties, such as atomic thinness and ionic activity. In this work, Co$_{3}$O$_{4}$ nanosheets were synthesized using a solvothermal method and integrated into artificial synapses. Based on the synaptic plasticity of the Co$_{3}$O$_{4}$ nanosheet-based memristive device, an artificial neural network (ANN) was designed and tested. A recognition accuracy of approximately 96 % was achieved for the Modified National Institute of Standards and Technology (MNIST) handwritten digit classification task using this ANN. These results highlight the potential of Co$_{3}$O$_{4}$ nanosheet-based artificial synapses and Al/Co$_{3}$O$_{4}$ nanosheet/ITO memristor devices as excellent material candidates for neuromorphic hardware.
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Received: 22 July 2025
Revised: 22 September 2025
Accepted manuscript online: 28 October 2025
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PACS:
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81.07.-b
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(Nanoscale materials and structures: fabrication and characterization)
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84.35.+i
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(Neural networks)
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85.35.-p
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(Nanoelectronic devices)
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73.40.Rw
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(Metal-insulator-metal structures)
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| Fund: This work was supported by the Natural Science Foundation of Shandong Province (Grant Nos. ZR2024MA019 and ZR2023QA106) and the “Pioneer” and “Leading Goose” R&D Program of Zhejiang (Grant No. 2023C01018). |
Corresponding Authors:
Jiajun Guo, Guangyu Wang, Liqian Wu
E-mail: guojiajun@lcu.edu.cn;wangguangyu@lcu.edu.cn;wulq@hdu.edu.cn
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| About author: 2025-128101-251257.pdf |
Cite this article:
Ying Li(李颖), Xiaofan Zhou(周晓凡), Jiajun Guo(郭家俊), Tong Chen(陈通), Xiaohui Zhang(张晓辉), Xia Xiao(肖夏), Guangyu Wang(王光宇), Mehran Khan Alam, Qi Zhang(张琪), and Liqian Wu(武力乾) Artificial synapse based on Co3O4 nanosheets for high-accuracy pattern recognition 2025 Chin. Phys. B 34 128101
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