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Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing.
ACS Appl Mater Interfaces. 2019 Dec 26; 11(51):48029-48038.AA

Abstract

The development of the information age has made resistive random access memory (RRAM) a critical nanoscale memristor device (MD). However, due to the randomness of the area formed by the conductive filaments (CFs), the RRAM MD still suffers from a problem of insufficient reliability. In this study, the memristor of Ag/ZrO2/WS2/Pt structure is proposed for the first time, and a layer of two-dimensional (2D) WS2 nanosheets was inserted into the MD to form 2D material and oxide double-layer MD (2DOMD) to improve the reliability of single-layer devices. The results indicate that the electrochemical metallization memory cell exhibits a highly stable memristive switching and concentrated ON- and OFF-state voltage distribution, high speed (∼10 ns), and robust endurance (>109 cycles). This result is superior to MDs with a single-layer ZrO2 or WS2 film because two layers have different ion transport rates, thereby limiting the rupture/rejuvenation of CFs to the bilayer interface region, which can greatly reduce the randomness of CFs in MDs. Moreover, we used the handwritten recognition dataset (i.e., the Modified National Institute of Standards and Technology (MNIST) database) for neuromorphic simulations. Furthermore, biosynaptic functions and plasticity, including spike-timing-dependent plasticity and paired-pulse facilitation, have been successfully achieved. By incorporating 2D materials and oxides into a double-layer MD, the practical application of RRAM MD can be significantly enhanced to facilitate the development of artificial synapses for brain-enhanced computing systems in the future.

Authors+Show Affiliations

National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China. Department of Materials Science and Engineering , National University of Singapore , Singapore 117576 , Singapore.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.Department of Electrical and Computer Engineering , Southern Illinois University Carbondale , Carbondale , Illinois 62901 , United States.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.Department of Electrical and Computer Engineering , Southern Illinois University Carbondale , Carbondale , Illinois 62901 , United States.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Machine Vision Engineering Technology Center of Hebei Province, Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electron and Information Engineering , Hebei University , Baoding 071002 , P. R. China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31789034

Citation

Yan, Xiaobing, et al. "Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing." ACS Applied Materials & Interfaces, vol. 11, no. 51, 2019, pp. 48029-48038.
Yan X, Qin C, Lu C, et al. Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing. ACS Appl Mater Interfaces. 2019;11(51):48029-48038.
Yan, X., Qin, C., Lu, C., Zhao, J., Zhao, R., Ren, D., Zhou, Z., Wang, H., Wang, J., Zhang, L., Li, X., Pei, Y., Wang, G., Zhao, Q., Wang, K., Xiao, Z., & Li, H. (2019). Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing. ACS Applied Materials & Interfaces, 11(51), 48029-48038. https://doi.org/10.1021/acsami.9b17160
Yan X, et al. Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing. ACS Appl Mater Interfaces. 2019 Dec 26;11(51):48029-48038. PubMed PMID: 31789034.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing. AU - Yan,Xiaobing, AU - Qin,Cuiya, AU - Lu,Chao, AU - Zhao,Jianhui, AU - Zhao,Rujie, AU - Ren,Deliang, AU - Zhou,Zhenyu, AU - Wang,Hong, AU - Wang,Jingjuan, AU - Zhang,Lei, AU - Li,Xiaoyan, AU - Pei,Yifei, AU - Wang,Gong, AU - Zhao,Qianlong, AU - Wang,Kaiyang, AU - Xiao,Zuoao, AU - Li,Hui, Y1 - 2019/12/13/ PY - 2019/12/4/pubmed PY - 2019/12/4/medline PY - 2019/12/3/entrez KW - WS2 nanosheets KW - artificial synapse KW - memristor KW - neuromorphic computing KW - resistive switching (RS) SP - 48029 EP - 48038 JF - ACS applied materials & interfaces JO - ACS Appl Mater Interfaces VL - 11 IS - 51 N2 - The development of the information age has made resistive random access memory (RRAM) a critical nanoscale memristor device (MD). However, due to the randomness of the area formed by the conductive filaments (CFs), the RRAM MD still suffers from a problem of insufficient reliability. In this study, the memristor of Ag/ZrO2/WS2/Pt structure is proposed for the first time, and a layer of two-dimensional (2D) WS2 nanosheets was inserted into the MD to form 2D material and oxide double-layer MD (2DOMD) to improve the reliability of single-layer devices. The results indicate that the electrochemical metallization memory cell exhibits a highly stable memristive switching and concentrated ON- and OFF-state voltage distribution, high speed (∼10 ns), and robust endurance (>109 cycles). This result is superior to MDs with a single-layer ZrO2 or WS2 film because two layers have different ion transport rates, thereby limiting the rupture/rejuvenation of CFs to the bilayer interface region, which can greatly reduce the randomness of CFs in MDs. Moreover, we used the handwritten recognition dataset (i.e., the Modified National Institute of Standards and Technology (MNIST) database) for neuromorphic simulations. Furthermore, biosynaptic functions and plasticity, including spike-timing-dependent plasticity and paired-pulse facilitation, have been successfully achieved. By incorporating 2D materials and oxides into a double-layer MD, the practical application of RRAM MD can be significantly enhanced to facilitate the development of artificial synapses for brain-enhanced computing systems in the future. SN - 1944-8252 UR - https://www.unboundmedicine.com/medline/citation/31789034/Robust_Ag/ZrO2/WS2/Pt_Memristor_for_Neuromorphic_Computing_ L2 - https://dx.doi.org/10.1021/acsami.9b17160 DB - PRIME DP - Unbound Medicine ER -
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