High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

doi:10.1088/1674-1056/ac673f

中国物理B ›› 2023, Vol. 32 ›› Issue (1): 17304-017304.doi: 10.1088/1674-1056/ac673f

High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

Xiao-Juan Lian(连晓娟)^1,2, Jin-Ke Fu(付金科)¹, Zhi-Xuan Gao(高志瑄)¹, Shi-Pu Gu(顾世浦)¹, and Lei Wang(王磊)^1,†

1 The College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
2 The National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

收稿日期:2022-01-16 修回日期:2022-04-02 接受日期:2022-04-14 出版日期:2022-12-08 发布日期:2022-12-08
通讯作者: Lei Wang E-mail:leiwang1980@njupt.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61804079 and 61964012), the open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology (Grant No. KFJJ20200102), the Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20211273 and BZ2021031), the Nanjing University of Posts and Telecommunications (Grant No. NY220112), and the Foundation of Jiangxi Science and Technology Department (Grant No. 20202ACBL21200).

High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

Xiao-Juan Lian(连晓娟)^1,2, Jin-Ke Fu(付金科)¹, Zhi-Xuan Gao(高志瑄)¹, Shi-Pu Gu(顾世浦)¹, and Lei Wang(王磊)^1,†

1 The College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
2 The National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Received:2022-01-16 Revised:2022-04-02 Accepted:2022-04-14 Online:2022-12-08 Published:2022-12-08
Contact: Lei Wang E-mail:leiwang1980@njupt.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 61804079 and 61964012), the open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology (Grant No. KFJJ20200102), the Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20211273 and BZ2021031), the Nanjing University of Posts and Telecommunications (Grant No. NY220112), and the Foundation of Jiangxi Science and Technology Department (Grant No. 20202ACBL21200).

摘要/Abstract

摘要： Threshold switching (TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage (0.38 V) and current (200 nA), an extremely steep slope (< 0.1 mV/dec), and a relatively large off/on ratio (> 10³). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.

关键词: memristors, artificial neurons, 2D MXene, Ge₂Sb₂Te₅

Abstract: Threshold switching (TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage (0.38 V) and current (200 nA), an extremely steep slope (< 0.1 mV/dec), and a relatively large off/on ratio (> 10³). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.

Key words: memristors, artificial neurons, 2D MXene, Ge₂Sb₂Te₅

中图分类号: (Electronic transport in interface structures)

73.40.-c

83.10.Tv (Structural and phase changes) 85.35.-p (Nanoelectronic devices) 87.19.lj (Neuronal network dynamics)

Xiao-Juan Lian(连晓娟), Jin-Ke Fu(付金科), Zhi-Xuan Gao(高志瑄),Shi-Pu Gu(顾世浦), and Lei Wang(王磊). High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors[J]. 中国物理B, 2023, 32(1): 17304-017304.

参考文献

[1] Jeong D S and Hwang C S 2018 Adv. Mater. 30 1704729
[2] Yu S 2018 Proc. IEEE 106 260
[3] Wang Z R, Joshi S, Savel'ev S E, Jiang H, Midya R, Lin P, Hu M, Ge N, Strachan J P, Li Z Y, Wu Q, Barnell M, Li G L, Xin H L, Williams R S, Xia Q F and Yang J J 2017 Nat. Mater. 16 101
[4] Qi G Y and Wang Z M 2021 Chin. Phys. B 30 120516
[5] Zhang X M, Zhuo Y, Luo Q, Wu Z H, Midya R, Wang Z R, Song W H, Wang R, Upadhyay N K, Fang Y L, Kiani F, Rao M Y, Yang Y, Xia Q F, Liu Q, Liu M and Yang J J 2020 Nat. Commun. 11 51
[6] Prezioso M, Mahmoodi M R, Bayat F M, Nili H, Kim H, Vincent A and Strukov D B 2018 Nat. Commun. 9 5311
[7] Panwar N, Rajendran B and Ganguly U 2017 IEEE Electron Device Lett. 38 740
[8] Lee D, Kwak M, Moon K, Choi W, Park J, Yoo J, Song J, Lim S, Sung C, Banerjee W and Hwang H 2019 Adv. Electron. Mater. 5 1800866
[9] Lashkare S, Chouhan S, Chavan T, Bhat A, Kumbhare P and Ganguly U 2018 IEEE Electron Device Lett. 39 484
[10] Tuma T, Pantazi A, Le Gallo M, Sebastian A and Eleftheriou E 2016 Nat. Nanotech. 11 693
[11] Dev D, Krishnaprasad A, Shawkat M S, He Z Z, Das S, Fan D L, Chung H S, Jung Y and Roy T 2020 IEEE Electron Device Lett. 41 936
[12] Chen Y H, Wang Y, Luo Y H, Liu X W, Wang Y Q, Gao F, Xu J G, Hu E T, Samanta S, Wan X, Lian X J, Xiao J and Tong Y 2019 IEEE Electron Device Lett. 40 1686
[13] Zhang X M, Wang W, Liu Q, Zhao X L, Wei J S, Cao R R, Yao Z H, Zhu X L, Zhang F, Lv H B, Long S B and Liu M 2018 IEEE Electron Device Lett. 39 308
[14] Lu Y F, Li Y, Li H Y, Wan T Q, Huang X D, He Y H and Miao X S 2020 IEEE Electron Device Lett. 41 1245
[15] Chen D and Huang S H 2016 Chin. Phys. B 25 117701
[16] Li Y J, Wu H Q, Gao B, Hua Q L, Zhang Z, Zhang W R and Qian H 2018 Chin. Phys. B 27 118502
[17] Li X B, Chen N K, Wang X P and Sun H B 2018 Adv. Funct. Mater. 28 1803380
[18] Cappelletti P, Annunziata R, Arnaud F, Disegni F, Maurelli A and Zuliani P 2020 J. Phys. D: Appl. Phys. 53 193002
[19] Dai Y, Wang H, Tao H L, Li W J, Li W M and Yang C L 2019 Physica E 114 113577
[20] Matsubara E, Okada S, Ichitsubo T, Kawaguchi T, Hirata A, Guan P F, Tokuda K, Tanimura K, Matsunaga T, Chen M W and Yamada N 2016 Phys. Rev. Lett. 117 135501
[21] Lv H B, Wan H J and Tang T A 2010 IEEE Electron Device Lett. 31 978
[22] Zhang Z Y, Wang Y Y, Wang G H, Mu J M, Ma M Y, He Y H, Yang R R and Li H L 2018 Sci. Rep. 8 12101
[23] Deleruyelle D, Putero M, Ouled-Khachroum T, Bocquet M, Coulet M V, Boddaert X, Calmes C and Muller C 2013 Solid State Electron. 79 159
[24] Huang Y H, Chen H A, Wu H H and Hsieh T E 2015 J. Appl. Phys. 117 014505
[25] Wang Q, Sun H J, Zhang J J, Xu X H and Miao X S 2012 J. Electron. Mater. 41 3417
[26] Xiao S X, Xie X D, Wen S P, Zeng Z G, Huang T W and Jiang J H 2018 Neurocomputing 272 677
[27] Bryja H, Grüner C, Gerlach J W, Behrens M, Ehrhardt M, Rauschenbach B and Lotnyk A 2020 J. Phys. D: Appl. Phys. 53 184002
[28] Lian X J, Shen X Y, Fu J K, Gao Z X, Wan X, Liu X Y, Hu E T, Xu J G and Tong Y 2020 Electronics 9 2098
[29] Lian X J, Shen X Y, Zhang M C, Xu J G, Gao F, Wan X, Hu E T, Guo Y F, Zhao J and Tong Y 2019 Appl. Phys. Lett. 115 063501
[30] Kresse G and Furthmüller J 1996 Phys. Rev. B 54 11169
[31] Perdew J P, Burke K and Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
[32] Blöchl P E 1994 Phys. Rev. B 50 17953
[33] Ji X L, Hao S, Pang K Y, Lim K G and Zhao R 2020 IEEE Electron Device Lett. 41 505
[34] Chen S C, Mahmoodi M R, Shi Y Y, Mahata C, Yuan B, Liang X H, Wen C, Hui F, Akinwande D J, Strukov D B and Lanza M 2020 Nat. Electron. 3 638
[35] Sokolov A, Ali M, Li H, Jeon Y R, Ko M J and Choi H H 2021 Adv. Electron. Mater. 7 2000866

High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

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