四方相多铁BiMnO3电控磁性的理论研究

袁野; 田博博; 段纯刚

doi:10.7498/aps.67.20180946

摘要

钙钛矿结构BiMnO3作为同时具有铁电性与铁磁性的多铁材料，在人工神经网络方面可以作为一种潜在的人工突触材料，从而设计出新型多铁人工突触器件.本文使用第一性原理计算的方法，分别研究了四方相BiMnO3在xy面内施加0.18%与4%应力条件下的铁电情况，以及Mn原子磁矩随着铁电极化强度变化的曲线.结果表明，在四方相多铁BiMnO3中，Mn原子磁矩会随着极化强度的增强而增大，表示其铁磁性可以在一定程度上由其铁电极化来进行调控，并且应力越大，其磁矩变化范围就越大.这一结果使得多铁BiMnO3在人工突触器件设计方面拥有潜在的应用价值，多铁性使其在作为人工突触器件材料中具有更多可调控的自由度，从而可用于模拟多突触连接.这可为将来构造类脑芯片打下一定的理论基础.

关键词:

多铁体 /
BiMnO3 /
人工突触

Abstract

Perovskite BiMnO3 with ferroelectric and ferromagnetic ordering simultaneously, as a kind of multiferroics, can be expected to have the coupling between the magnetic and dielectric properties as well as their control by the application of electric fields. This advantage can make BiMnO3 a good candidate for an artificial synapse material. Under the framework of the density functional theory, in this paper we adopt the generalized gradient approximation (GGA+U) plane wave pseudopotential method to calculate the ferroelectricity double-well potential curves and magnetic moments of Mn of tetragonal BiMnO3, with 0.18% and 4% strain exerted in its x-y plane. The results show that the magnetic moment of Mn monotonically increases from paraelectric state to ferroelectric state. It means that the ferromagnetic property of tetragonal BiMnO3 can be controlled by the intensity of polarization. The greater the stress, the greater the range of magnetic moment is. This would imply that the multiferroic artificial synapse device based on BiMnO3 can bring another degree of freedom into designing the complex cognitive systems of artificial intelligence in the future.

Keywords:

作者及机构信息

1.
华东师范大学信息科学技术学院, 极化材料与器件教育部重点实验室, 上海 200241

通信作者: 田博博, bbtian@ee.ecnu.edu.cn

基金项目: 上海科技创新基础研究项目（批准号：17JC1402500）、上海扬帆科技人才项目（批准号：17YF1404200）和中国博士后创新人才项目（批准号：BX201600052）资助的课题.

Authors and contacts

1.
Key Laboratory of Polar Materials and Devices, Ministry of Education, School of Information Science Technology, East China Normal University, Shanghai 200241, China

Corresponding author: Tian Bo-Bo, bbtian@ee.ecnu.edu.cn

Funds: Project supported by the Shanghai Science and Technology Innovation Action Plan, China (Grant No. 17JC1402500), the Shanghai Sailing Program, China (Grant No. 17YF1404200), and the National Postdoctoral Program for Innovative Talents, China (Grant No. BX201600052).

参考文献

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[21]	Lequeux S, Sampaio J, Cros V, Yakushiji K, Fukushima A, Matsumoto R, Kubota H, Yuasa S, Grollier J 2016 Sci. Rep. 6 31510
[22]	Biswas A K, Atulasimha J, Bandyopadhyay S 2015 Nanotechnology 26 285201
[23]	Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
[24]	Kresse G, Furthmuller J 1996 Comput. Mater. Sci. 6 15
[25]	Kresse G, Joubert D 1999 Phys. Rev. B 59 1758
[26]	Kresse G, Furthmuller J 1996 Phys. Rev. B 54 11169
[27]	Blchl P E, Jepsen O, Andersen O K 1994 Phys. Rev. B 49 16223
[28]	Dudarev S L, Dudarev S L, Botton G A, Savrasov S Y, Humphreys C J, Sutton A P 1998 Phys. Rev. B 57 1505
[29]	Gao Y C, Duan C G, Tang X D, Hu Z G, Yang P, Zhu Z, Chu J 2013 J. Phys.: Condens. Matter 25 165901

施引文献

[1]	Yang J J, Strukov D B, Stewart D R 2013 Nat. Nanotechnol. 8 13
[2]	Yang Y, Wen J, Guo L, Wan X, Du P, Feng P, Shi Y, Wan Q 2016 ACS Appl. Mater. Interfaces 8 30281
[3]	Hebb D O 1949 The Organization of Behavior: A Neuropsychological Theory (New York: John Wiley and Sons, Inc.)
[4]	Kandel E R 2001 Science 294 1030
[5]	Burke S N, Barnes C A 2006 Nat. Rev. Neurosci. 7 30
[6]	Merolla P A, Arthur J V, Alvarez-Icaza R 2014 Science 345 668
[7]	Versace M, Chandler B 2010 IEEE Spectrum 47 30
[8]	Smith L S 2006 Handbook of Nature-Inspired and Innovative Computing: Integrating Classical Models with Emerging Technologies (New York: Springer) pp433-475
[9]	Indiveri G, Chicca E, Douglas R 2006 IEEE Trans. Neural Networks 17 211
[10]	Song S, Miller K D, Abbott L F 2000 Nature Neurosci. 3 919
[11]	Bi G Q, Poo M M 1998 J. Neurosci. 18 10464
[12]	Douglas R, Mahowald M, Mead C 1995 Annu. Rev. Neurosci. 18 255
[13]	Jo S H, Chang T, Ebong I, Bhadviya B B, Mazumder P, Lu W 2010 Nano Lett. 10 1297
[14]	Boyn S, Grollier J, Lecerf G, Xu B, Locatelli N, Fusil S, Girod S, Carretero C, Garcia K, Xavier S, Tomas J, Bellaiche L, Bibes M, Barthlmy A, Saghi S, Garcia V 2017 Nat. Commun. 8 14736
[15]	Chanthbouala A, Garcia V, Cherifi R O, Bouzehouane K, Fusil S, Moya X, Xavier S, Yamada H, Deranlot C, Mathur N D, Bibes M, Barthlmy A, Grollier J 2012 Nat. Mater. 11 860
[16]	Kim D J, Lu H, Ryu S, Bark C W, Eom C B, Tsymbal E Y, Gruverman A 2012 Nano Lett. 12 5697
[17]	Hill N A, Rabe K M 1999 Phys. Rev. B 59 8759
[18]	Seshadri R, Hill N A 2001 Chem. Mater. 13 2892
[19]	Plov L, Chandra P, Rabe K M 2010 Phys. Rev. B 82 075432
[20]	Chanthbouala A, Matsumoto R, Grollier J, Cros V, Anane A, Fert A, Khvalkovskiy A V, Zvezdin K A, Nishimura K, Nagamine Y, Maehara H, Tsunekawa K, Fukushima A, Yuasa S 2011 Nat. Phys. 7 626
[21]	Lequeux S, Sampaio J, Cros V, Yakushiji K, Fukushima A, Matsumoto R, Kubota H, Yuasa S, Grollier J 2016 Sci. Rep. 6 31510
[22]	Biswas A K, Atulasimha J, Bandyopadhyay S 2015 Nanotechnology 26 285201
[23]	Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
[24]	Kresse G, Furthmuller J 1996 Comput. Mater. Sci. 6 15
[25]	Kresse G, Joubert D 1999 Phys. Rev. B 59 1758
[26]	Kresse G, Furthmuller J 1996 Phys. Rev. B 54 11169
[27]	Blchl P E, Jepsen O, Andersen O K 1994 Phys. Rev. B 49 16223
[28]	Dudarev S L, Dudarev S L, Botton G A, Savrasov S Y, Humphreys C J, Sutton A P 1998 Phys. Rev. B 57 1505
[29]	Gao Y C, Duan C G, Tang X D, Hu Z G, Yang P, Zhu Z, Chu J 2013 J. Phys.: Condens. Matter 25 165901

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