3D-structured mesoporous silica memristors for neuromorphic switching and reservoir computing

Jaafar, Ayoub H.; Shao, Li; Dai, Peng; Zhang, Tongjun; Han, Yisong; Beanland, Richard; Kemp, Neil T.; Bartlett, Philip N.; Hector, Andrew L.; Huang, Ruomeng

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.

Citation

Jaafar, A. H., Shao, L., Dai, P., Zhang, T., Han, Y., Beanland, R., …Huang, R. (2022). 3D-structured mesoporous silica memristors for neuromorphic switching and reservoir computing. Nanoscale, 14(46), 17170-17181. https://doi.org/10.1039/d2nr05012a

• Journal Article Type: Article
• Acceptance Date: Nov 10, 2022
• Online Publication Date: Nov 10, 2022
• Publication Date: Dec 14, 2022
• Deposit Date: Nov 23, 2022
• Publicly Available Date: Nov 24, 2022
• Journal: Nanoscale
• Print ISSN: 2040-3364
• Electronic ISSN: 2040-3372
• Publisher: Royal Society of Chemistry (RSC)
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 46
• Pages: 17170-17181
• DOI: https://doi.org/10.1039/d2nr05012a
• Keywords: General Materials Science
• Public URL: https://nottingham-repository.worktribe.com/output/14034508
• Publisher URL: https://pubs.rsc.org/en/content/articlelanding/2022/NR/D2NR05012A