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A carbon-based memristor design for associative learning activities and neuromorphic computing.
Nanoscale. 2020 Jul 02; 12(25):13531-13539.N

Abstract

Carbon quantum dots (QDs) have attracted significant interest due to their excellent electronic properties and wide application prospects. However, the application of carbon QDs has been rarely reported in memristors. Here, a memristor model with carbon conductive filaments (CFs) is proposed for the first time based on carbon quantum dots. The CF-based devices exhibited excellent resistive switching performance, in particular a narrow range of SET and RESET voltages and good power efficiency and retention properties. These devices could also emulate important biological synapse performances, such as the transition from short-term plasticity (STP) to long-term potentiation (LTP) behaviors, long-term depression (LTD) behavior, and four types of spike-timing-dependent plasticity (STDP) learning rules. Interestingly, Pavlovian associative learning functions were also reliably demonstrated in the memristor device (MD). The digit recognition ability of the MDs was evaluated though a single-layer perceptron model, in which the recognition accuracy of digits reached 92.63% after 250 training iterations. The transmission electron microscopy (TEM) results evidenced that the carbon CF was found in the MD at the "ON" state. Thus, this new carbon CF-based mechanism for memristors provides a new idea for achieving better neuromorphic MDs and applications.

Authors+Show Affiliations

National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. of China. yanxiaobing@ime.ac.cn.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32555882

Citation

Pei, Yifei, et al. "A Carbon-based Memristor Design for Associative Learning Activities and Neuromorphic Computing." Nanoscale, vol. 12, no. 25, 2020, pp. 13531-13539.
Pei Y, Zhou Z, Chen AP, et al. A carbon-based memristor design for associative learning activities and neuromorphic computing. Nanoscale. 2020;12(25):13531-13539.
Pei, Y., Zhou, Z., Chen, A. P., Chen, J., & Yan, X. (2020). A carbon-based memristor design for associative learning activities and neuromorphic computing. Nanoscale, 12(25), 13531-13539. https://doi.org/10.1039/d0nr02894k
Pei Y, et al. A Carbon-based Memristor Design for Associative Learning Activities and Neuromorphic Computing. Nanoscale. 2020 Jul 2;12(25):13531-13539. PubMed PMID: 32555882.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A carbon-based memristor design for associative learning activities and neuromorphic computing. AU - Pei,Yifei, AU - Zhou,Zhenyu, AU - Chen,Andy Paul, AU - Chen,Jingsheng, AU - Yan,Xiaobing, PY - 2020/6/20/pubmed PY - 2020/6/20/medline PY - 2020/6/20/entrez SP - 13531 EP - 13539 JF - Nanoscale JO - Nanoscale VL - 12 IS - 25 N2 - Carbon quantum dots (QDs) have attracted significant interest due to their excellent electronic properties and wide application prospects. However, the application of carbon QDs has been rarely reported in memristors. Here, a memristor model with carbon conductive filaments (CFs) is proposed for the first time based on carbon quantum dots. The CF-based devices exhibited excellent resistive switching performance, in particular a narrow range of SET and RESET voltages and good power efficiency and retention properties. These devices could also emulate important biological synapse performances, such as the transition from short-term plasticity (STP) to long-term potentiation (LTP) behaviors, long-term depression (LTD) behavior, and four types of spike-timing-dependent plasticity (STDP) learning rules. Interestingly, Pavlovian associative learning functions were also reliably demonstrated in the memristor device (MD). The digit recognition ability of the MDs was evaluated though a single-layer perceptron model, in which the recognition accuracy of digits reached 92.63% after 250 training iterations. The transmission electron microscopy (TEM) results evidenced that the carbon CF was found in the MD at the "ON" state. Thus, this new carbon CF-based mechanism for memristors provides a new idea for achieving better neuromorphic MDs and applications. SN - 2040-3372 UR - https://www.unboundmedicine.com/medline/citation/32555882/A_carbon_based_memristor_design_for_associative_learning_activities_and_neuromorphic_computing_ L2 - https://doi.org/10.1039/d0nr02894k DB - PRIME DP - Unbound Medicine ER -
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