Voltage-controllable magnetic skyrmion dynamics for spiking neuron device applications

doi:10.1088/1674-1056/ac05b0

Abstract Voltage-controlled magnetic skyrmions have attracted special attention because they satisfy the requirements for well-controlled high-efficiency and energy saving for future skyrmion-based neuron device applications. In this work, we propose a compact leaky-integrate-fire (LIF) spiking neuron device by using the voltage-driven skyrmion dynamics in a multiferroic nanodisk structure. The skyrmion dynamics is controlled by well tailoring voltage-induced piezostrains, where the skyrmion radius can be effectively modulated by applying the piezostrain pulses. Like the biological neuron, the proposed skyrmionic neuron will accumulate a membrane potential as skyrmion radius is varied by inputting the continuous piezostrain spikes, and the skyrmion radius will return to the initial state in the absence of piezostrain. Therefore, this skyrmion radius-based membrane potential will reach a definite threshold value by the strain stimuli and then reset by removing the stimuli. Such the LIF neuronal functionality and the behaviors of the proposed skyrmionic neuron device are elucidated through the micromagnetic simulation studies. Our results may benefit the utilization of skyrmionic neuron for constructing the future energy-efficient and voltage-tunable spiking neural networks.

Keywords: magnetic skyrmion leaky-integrate-fire multiferroic heterostructure artificial neuron

Received: 20 February 2021
Revised: 18 May 2021
Accepted manuscript online: 27 May 2021

PACS:	85.75.-d	(Magnetoelectronics; spintronics: devices exploiting spin polarized transport or integrated magnetic fields)
	84.35.+i	(Neural networks)
	75.70.Kw	(Domain structure (including magnetic bubbles and vortices))
	75.78.Cd	(Micromagnetic simulations ?)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11902316, 51902300, and 11972333) and the Natural Science Foundation of Zhejiang Province, China (Grant Nos. LQ19F010005, LY21F010011, and LZ19A020001).

Corresponding Authors: Guo-Liang Yu, Hao-Miao Zhou
E-mail: glyu@cjlu.edu.cn;hmzhou@cjlu.edu.cn

Cite this article:

Ming-Min Zhu(朱明敏), Shu-Ting Cui(崔淑婷), Xiao-Fei Xu(徐晓飞), Sheng-Bin Shi(施胜宾), Di-Qing Nian(年迪青), Jing Luo(罗京), Yang Qiu(邱阳), Han Yang(杨浛), Guo-Liang Yu(郁国良), and Hao-Miao Zhou (周浩淼) Voltage-controllable magnetic skyrmion dynamics for spiking neuron device applications 2022 Chin. Phys. B 31 018503

[1] Zhang D, Zeng L, Cao K, Wang M, Peng S, Zhang Y, Zhang Y, Klein J O, Wang Y and Zhao W 2016 IEEE Trans. Biomed. Circuits Syst. 10 828
[2] Hirohata A, Yamada K, Nakatani Y, Prejbeanu I L, Dièny B, Pirro P and Hillebrands B 2020 J. Magn. Magn. Mater. 509 166711
[3] Yang K, Malhotra A, Lu S and Sengupta A 2020 IEEE Trans. Electron Dev. 67 1340
[4] Wang C, Wang Z, Wang M, Zhang X, Zhang Y and Zhao W 2020 IEEE Trans. Electron Dev. 67 2621
[5] Brigner W H, Friedman J S, Hassan N, Jiang-Wei L, Hu X, Saha D, Bennett C H, Marinella M J, Incorvia J A C and Garcia-Sanchez F 2019 IEEE Trans. Electron Dev. 66 4970
[6] Merolla P A, Arthur J V, Alvarez-Icaza R, Cassidy A S, Sawada J, Akopyan F, Jackson B L, Imam N, Guo C, Nakamura Y, Brezzo B, Vo I, Esser S K, Appuswamy R, Taba B, Amir A, Flickner M D, Risk W P, Manohar R and Modha D S 2014 Science 345 668
[7] Akopyan F, Sawada J, Cassidy A, Alvarez-Icaza R, Arthur J, Merolla P, Imam N, Nakamura Y, Datta P, Nam G, Taba B, Beakes M, Brezzo B, Kuang J B, Manohar R, Risk W P, Jackson B and Modha D S 2015 IEEE Trans. Computer-Aided Design Integr. Circuits Syst. 34 1537
[8] Lee D, Kwak M, Moon K, Choi W, Park J, Yoo J, Song J, Lim S, Sung C and Banerjee W 2019 Adv. Electron. Mater. 5 1800866
[9] Grollier J, Querlioz D, Camsari K Y, Everschor-Sitte K, Fukami S and Stiles M D 2020 Nat. Electron. 3 360
[10] Fukami S and Ohno H 2018 J. Appl. Phys. 124 151904
[11] Sengupta A, Shim Y and Roy K 2017 IEEE Trans. Biomed. Circuits Syst. 10 1152
[12] Fert A, Reyren N and Cros V 2017 Nat. Rev. Mater. 2 17031
[13] Zhang X, Zhou Y, Mee Song K, Park T E, Xia J, Ezawa M, Liu X, Zhao W, Zhao G and Woo S 2020 J. Phys.: Condens. Matter 32 143001
[14] Chen M C, Sengupta A and Roy K 2018 IEEE Trans. Magn. 54 1500207
[15] Azam M A, Bhattacharya D, Querlioz D and Atulasimha J 2018 J. Appl. Phys. 124 152122
[16] Prychynenko D, Sitte M, Litzius K, Krüger B, Bourianoff G, Kläui M, Sinova J and Everschor-Sitte K 2018 Phys. Rev. Appl. 9 014034
[17] Li S, Kang W, Huang Y, Zhang X, Zhou Y and Zhao W 2017 Nanotechnology 28 31LT01
[18] Chen X, Kang W, Zhu D, Zhang X, Lei N, Zhang Y, Zhou Y and Zhao W 2018 Nanoscale 10 6139
[19] Liang X, Zhang X, Xia J, Ezawa M, Zhao Y, Zhao G and Zhou Y 2020 Appl. Phys. Lett. 116 122402
[20] Yanes R, Garcia-Sanchez F, Luis R F, Martinez E, Raposo V, Torres L and Lopez-Diaz L 2019 Appl. Phys. Lett. 115 132401
[21] Li Z, Zhang Y, Huang Y, Wang C, Zhang X, Liu Y, Zhou Y, Kang W, Koli S C and Lei N 2018 J. Magn. Magn. Mater. 455 19
[22] Wang Y, Wang L, Xia J, Lai Z, Tian G, Zhang X, Hou Z, Gao X, Mi W, Feng C, Zeng M, Zhou G, Yu G, Wu G, Zhou Y, Wang W, Zhang X X and Liu J 2020 Nat. Commun. 11 3577
[23] Ba Y, Zhuang S, Zhang Y, Wang Y, Gao Y, Zhou H, Chen M, Sun W, Liu Q, Chai G, Ma J, Zhang Y, Tian H, Du H, Jiang W, Nan C, Hu J M and Zhao Y 2021 Nat. Commun. 12 322
[24] Luo S, Xu N, Guo Z, Zhang Y, Hong J and You L 2019 IEEE Electron Dev. Lett. 40 635
[25] Yu Z, Shen M, Zeng Z, Liang S, Liu Y, Chen M, Zhang Z, Lu Z, You L, Yang X, Zhang Y and Xiong R 2020 Nanoscale Adv. 2 1309
[26] Tang J, Kong L, Wang W, Du H and Tian M 2019 Chin. Phys. B 28 087503
[27] Zhang S L, Wang W W, Burn D M, Peng H, Berger H, Bauer A, Pfleiderer C, van der Laan G and Hesjedal T 2018 Nat. Commun. 9 2115
[28] Dong D, Cai L, Li C, Liu B, Li C and Liu J 2019 J. Phys. D: Appl. Phys. 52 295001
[29] Wang Q, Domann J, Yu G, Barra A, Wang K L and Carman G P 2018 Phys. Rev. Appl. 10 034052
[30] Voto M, Lopez-Diaz L and Martinez E 2017 Sci. Rep. 7 13559
[31] Hu J M, Yang T and Chen L Q 2020 Acta Mater. 183 145

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Voltage-controllable magnetic skyrmion dynamics for spiking neuron device applications

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