Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing
Neuromorphic nanowire networks are of broad interest for applications in burgeoning memristive devices and neuromorphic computing areas due to their interesting features such as neural-like topology and nonlinear dynamics. However, the complexity of the neuromorphic nanowire network’s behavior and i...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Chemical Society
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114586/ http://psasir.upm.edu.my/id/eprint/114586/1/114586.pdf |
| _version_ | 1848866537861021696 |
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| author | Weng, Zhengjin Ji, Tianyi Yu, Yanling Fang, Yong Lei, Wei Shafie, Suhaidi Jindapetch, Nattha Zhao, Zhiwei |
| author_facet | Weng, Zhengjin Ji, Tianyi Yu, Yanling Fang, Yong Lei, Wei Shafie, Suhaidi Jindapetch, Nattha Zhao, Zhiwei |
| author_sort | Weng, Zhengjin |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Neuromorphic nanowire networks are of broad interest for applications in burgeoning memristive devices and neuromorphic computing areas due to their interesting features such as neural-like topology and nonlinear dynamics. However, the complexity of the neuromorphic nanowire network’s behavior and in materia reservoir computing with imperfect device performance still hampers a straight transfer into emerging computing applications. Herein, reliable memristive devices based on unique necklace-like structure Ag@TiO2 nanowire networks are demonstrated for neuromorphic learning and reservoir computing. The memristive devices utilizing necklace-like structure Ag@TiO2 nanowire networks exhibit stable volatile threshold switching characteristics, with a ratio of up to 105, low threshold voltage (<1 V), good endurance, and high uniformity. Besides, the devices have been successfully used to emulate diverse functions of synapses by exploiting the Ag filament dynamics within the nanowire network, including short-term plasticity, and transition from short-term plasticity to long-term plasticity. The nanowire networks that offer nonlinear and short-term dynamics are further harnessed to build a reservoir computing system for the waveform classification task, manifesting its great potential for the development of next-generation reservoir hardware. |
| first_indexed | 2025-11-15T14:22:11Z |
| format | Article |
| id | upm-114586 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:22:11Z |
| publishDate | 2024 |
| publisher | American Chemical Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1145862025-03-10T02:00:09Z http://psasir.upm.edu.my/id/eprint/114586/ Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing Weng, Zhengjin Ji, Tianyi Yu, Yanling Fang, Yong Lei, Wei Shafie, Suhaidi Jindapetch, Nattha Zhao, Zhiwei Neuromorphic nanowire networks are of broad interest for applications in burgeoning memristive devices and neuromorphic computing areas due to their interesting features such as neural-like topology and nonlinear dynamics. However, the complexity of the neuromorphic nanowire network’s behavior and in materia reservoir computing with imperfect device performance still hampers a straight transfer into emerging computing applications. Herein, reliable memristive devices based on unique necklace-like structure Ag@TiO2 nanowire networks are demonstrated for neuromorphic learning and reservoir computing. The memristive devices utilizing necklace-like structure Ag@TiO2 nanowire networks exhibit stable volatile threshold switching characteristics, with a ratio of up to 105, low threshold voltage (<1 V), good endurance, and high uniformity. Besides, the devices have been successfully used to emulate diverse functions of synapses by exploiting the Ag filament dynamics within the nanowire network, including short-term plasticity, and transition from short-term plasticity to long-term plasticity. The nanowire networks that offer nonlinear and short-term dynamics are further harnessed to build a reservoir computing system for the waveform classification task, manifesting its great potential for the development of next-generation reservoir hardware. American Chemical Society 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/114586/1/114586.pdf Weng, Zhengjin and Ji, Tianyi and Yu, Yanling and Fang, Yong and Lei, Wei and Shafie, Suhaidi and Jindapetch, Nattha and Zhao, Zhiwei (2024) Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing. ACS Applied Nano Materials, 7 (17). pp. 1-10. ISSN 2574-0970; eISSN: 2574-0970 https://pubs.acs.org/doi/10.1021/acsanm.4c04063 10.1021/acsanm.4c04063 |
| spellingShingle | Weng, Zhengjin Ji, Tianyi Yu, Yanling Fang, Yong Lei, Wei Shafie, Suhaidi Jindapetch, Nattha Zhao, Zhiwei Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing |
| title | Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing |
| title_full | Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing |
| title_fullStr | Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing |
| title_full_unstemmed | Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing |
| title_short | Memristive devices based on necklace-like structure Ag@TiO2 nanowire networks for neuromorphic learning and reservoir computing |
| title_sort | memristive devices based on necklace-like structure ag@tio2 nanowire networks for neuromorphic learning and reservoir computing |
| url | http://psasir.upm.edu.my/id/eprint/114586/ http://psasir.upm.edu.my/id/eprint/114586/ http://psasir.upm.edu.my/id/eprint/114586/ http://psasir.upm.edu.my/id/eprint/114586/1/114586.pdf |