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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...

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Main Authors: Weng, Zhengjin, Ji, Tianyi, Yu, Yanling, Fang, Yong, Lei, Wei, Shafie, Suhaidi, Jindapetch, Nattha, Zhao, Zhiwei
格式: Article
語言:English
出版: American Chemical Society 2024
在線閱讀:http://psasir.upm.edu.my/id/eprint/114594/1/114594.pdf
http://psasir.upm.edu.my/id/eprint/114594/
https://pubs.acs.org/doi/10.1021/acsanm.4c04063
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spelling my.upm.eprints.1145942025-03-10T02:02:41Z http://psasir.upm.edu.my/id/eprint/114594/ 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/114594/1/114594.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. 21018-21025. ISSN 2574-0970; eISSN: 2574-0970 https://pubs.acs.org/doi/10.1021/acsanm.4c04063 10.1021/acsanm.4c04063
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Weng, Zhengjin
Ji, Tianyi
Yu, Yanling
Fang, Yong
Lei, Wei
Shafie, Suhaidi
Jindapetch, Nattha
Zhao, Zhiwei
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
author_facet Weng, Zhengjin
Ji, Tianyi
Yu, Yanling
Fang, Yong
Lei, Wei
Shafie, Suhaidi
Jindapetch, Nattha
Zhao, Zhiwei
author_sort Weng, Zhengjin
title 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_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_sort memristive devices based on necklace-like structure ag@tio2 nanowire networks for neuromorphic learning and reservoir computing
publisher American Chemical Society
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/114594/1/114594.pdf
http://psasir.upm.edu.my/id/eprint/114594/
https://pubs.acs.org/doi/10.1021/acsanm.4c04063
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score 13.251813

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