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3D-structured mesoporous silica memristors for neuromorphic switching and reservoir computing
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Jaafar, Ayoub H., Shao, Li, Dai, Peng, Zhang, Tongjun, Han, Yisong, Beanland, Richard, Kemp, Neil T., Bartlett, Philip N., Hector, Andrew L. and Huang, Ruomeng (2022) 3D-structured mesoporous silica memristors for neuromorphic switching and reservoir computing. Nanoscale, 14 (46). pp. 17170-17181. doi:10.1039/d2nr05012a ISSN 2040-3364.
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WRAP-3D-structured-mesoporous-silica-memristors-neuromorphic-switching-reservoir-computing-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution. Download (3829Kb) | Preview |
Official URL: https://doi.org/10.1039/d2nr05012a
Abstract
Memristors are emerging as promising candidates for practical application in reservoir computing systems that are capable of temporal information processing. Here, we experimentally implement a physical reservoir computing system using resistive memristors based on three-dimensional (3D)-structured mesoporous silica (mSiO2) thin films fabricated by a low cost, fast and vacuum-free sol–gel technique. The in situ learning capability and a classification accuracy of 100% on a standard machine learning dataset are experimentally demonstrated. The volatile (temporal) resistive switching in diffusive memristors arises from the formation and subsequent spontaneous rupture of conductive filaments via diffusion of Ag species within the 3D-structured nanopores of the mSiO2 thin film. Besides volatile switching, the devices also exhibit a bipolar non-volatile resistive switching behavior when the devices are operated at a higher compliance current level. The implementation of mSiO2 thin films opens the route to fabricate a simple and low cost dynamic memristor with a temporal information process functionality, which is essential for neuromorphic computing applications.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Memristors, Mesoporous materials, Silica, Thin films, Neuromorphics | ||||||||||||
| Journal or Publication Title: | Nanoscale | ||||||||||||
| Publisher: | Royal Society of Chemistry | ||||||||||||
| ISSN: | 2040-3364 | ||||||||||||
| Official Date: | 2022 | ||||||||||||
| Dates: |
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| Volume: | 14 | ||||||||||||
| Number: | 46 | ||||||||||||
| Page Range: | pp. 17170-17181 | ||||||||||||
| DOI: | 10.1039/d2nr05012a | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
| Date of first compliant deposit: | 20 December 2022 | ||||||||||||
| Date of first compliant Open Access: | 20 December 2022 | ||||||||||||
| RIOXX Funder/Project Grant: |
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