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Research Article

The resistance switching performance of the memristor improved effectively by inserting carbon quantum dots (CQDs) for digital information processing

Tianqi Yu1,§, Jie Li1,§, Wei Lei1, Suhaidi Shafe2, Mohd Nazim Mohtar2, Nattha Jindapetch3, Paphavee van Dommelen4, Zhiwei Zhao1( )
Joint International Research Laboratory of Information Display and Visualization, School of Electronic Science and Engineering, Southeast University, Nanjing 210096, China
Institute of Nanoscience and Nanotechnology, University Putra Malaysia, Serdang, Selangor 43400, Malaysia
Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
Division of Physical Science, Faculty of Science, Prince of Songkla University, Hat Yai Campus 15 Karnjanavanich, Hat Yai, Songkhla 90110, Thailand

§ Tianqi Yu and Jie Li contributed equally to this work.

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Graphical Abstract

The performance of memristors is improved by inserting carbon quantum dots (CQDs) for high-precision recognition and convolutional image processing.

Abstract

As an emerging information device that adapts to development of the big data era, memristor has attracted much attention due to its advantage in processing massive data. However, the nucleation and growth of conductive filaments often exhibit randomness and instability, which undoubtedly leads to a wide and discrete range of switching parameters, damaging the electrical performance of device. In this work, a strategy of inserting carbon quantum dots (CQDs) into graphene oxide (GO) resistance layer is utilized to improve the stability of the switching parameters and the reliability of the device is improved. Compared with GO-based devices, GO/CQDs/GO-based devices exhibit a more stable resistance switching curve, low power, lower and more concentrated threshold voltage parameters with lower variation coefficient, faster switching speed, and more stable retention and endurance. The cause-inducing performance improvement may be attributed to the local electric field generated by CQDs in resistance switching that effectively guides the formation and rupture of conductive filaments, which optimizes the effective migration distance of Ag+, thereby improving the uniformity of resistance switching. Additionally, a convolutional neural network model is constructed to identify the CIFAR-10 data set, showing the high recognition accuracy of online and offline learning. The cross-kernel structure is used to further implement convolutional image processing through multiplication and accumulation operations. This work provides a solution to improve the performance of memristors, which can contribute to developing digital information processing.

Keywords

convolutional neural networkmemristoruniformitycarbon quantum dotsconvolutional image processing

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Nano Research
Volume 17 Issue 9,
September 2024
Pages 8438-8446
DOI: 10.1007/s12274-024-6801-4
Cite this article:
Yu T, Li J, Lei W, et al. The resistance switching performance of the memristor improved effectively by inserting carbon quantum dots (CQDs) for digital information processing. Nano Research, 2024, 17(9): 8438-8446. https://doi.org/10.1007/s12274-024-6801-4
Topics:
Memristor

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Received: 01 May 2024
Revised: 23 May 2024
Accepted: 29 May 2024
Published: 13 July 2024
© Tsinghua University Press 2024
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