Activation function and computing performance of spin neuron driven by magnetic field and strain

Yuan Jia-Hui; Yang Xiao-Kuo; Zhang Bin; Chen Ya-Bo; Zhong Jun; Wei Bo; Song Ming-Xu; Cui Huan-Qing

doi:10.7498/aps.70.20210611

Abstract

The spin neuron is an emerging artificial neural device which has many advantages such as ultra-low power consumption, strong nonlinearity, and high integration. Besides, it has ability to remember and calculate at the same time. So it is seen as a suitable and excellent candidate for the new generation of neural network. In this paper, a spin neuron driven by magnetic field and strain is proposed. The micromagnetic model of the device is realized by using the OOMMF micromagnetic simulation software, and the numerical model of the device is also established by using the LLG equation. More importantly, a three-layer neural network is composed of spin neurons constructed respectively using three materials (Terfenol-D, FeGa, Ni). It is used to study the activation functions and the ability to recognize the MNIST handwritten datasets.c Results show that the spin neuron can successfully achieve the random magnetization switching to simulate the activation behavior of the biological neuron. Moreover, the results show that if the ranges of the inputting magnetic fields are different, the three materials' neurons can all reach the saturation accuracy. It is expected to replace the traditional CMOS neuron. And the overall power consumption of intelligent computing can be further reduced by using appropriate materials. If we input the magnetic fields in the same range, the recognition speed of the spin neuron made of Ni is the slowest in the three materials. The results can establish a theoretical foundation for the design and the applications of the new artificial neural networks and the intelligent circuits.

Keywords:

Authors and contacts

1.
Fundamentals Department, Air Force Engineering University, Xi’an 710051, China
2.
College of Computer, National University of Defense, Changsha 410005, China
3.
Airforce Command College, Beijing 100097, China

Corresponding author: Yang Xiao-Kuo, yangxk0123@163.com

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 11975311) and the Natural Science Basic Research Program of Shaanxi, China (Grant Nos. 2021JM-221, 2020JQ-470).

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References

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Cited By

图 1 磁场辅助磁弹时钟神经元

Figure 1. Magnetic field assisted strain-mediated neuron.

DownLoad: Full-Size Img PowerPoint

图 2 磁化翻转原理图

Figure 2. Schematic of magnetization switching.

DownLoad: Full-Size Img PowerPoint

图 3 动态磁化过程　(a) 磁化初始方向为–y; (b) 施加30 MPa应力, 磁化翻转90°; 撤去应力, 施加10 mT磁场; (c)方向相反, 磁化翻转180°; (d)方向相同, 磁化翻转0°

Figure 3. Magnetization process: (a) Initial direction of magnetization is –y; (b) a 30 MPa strain is applied and then 90° switching is achieved, removing strain and applying a 10 mT magnetic field; (c) 180° magnetization switching; (d) 0° magnetization switching.

DownLoad: Full-Size Img PowerPoint

图 4 磁化矢量随时间变化曲线图　(a) 1 ns时, 施加磁场沿y轴正方向时的磁化矢量变化图; (b) 1 ns时, 施加磁场沿y轴负方向的磁化矢量变化图

Figure 4. Magnetization vector with time: (a) When t = 1 ns, a magnetic field is applied in the direction along +y; (b) when t = 1 ns, a magnetic field is applied in the direction along –y.

DownLoad: Full-Size Img PowerPoint

图 5 室温下随机磁化翻转的动态磁化过程　(a) 180°磁化翻转; (b) 0°磁化翻转

Figure 5. Magnetization dynamics at room temperature: (a) 180° magnetization switching; (b) 0° magnetization switching.

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图 6 180°磁化翻转概率与输入磁场的关系, 磁致伸缩层材料为(a) Terfenol-D, (b) FeGa, (c) Ni

Figure 6. 180° magnetization switching probability versus magnetic field, the magnetostrictive layer material is (a) Terfenol-D, (b) FeGa, (c) Ni.

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图 7 基于磁场辅助磁弹时钟的自旋神经元的三层神经网络结构示意图

Figure 7. Three-layer neural network based on magnetic field + strain spin neurons.

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图 8 不同材料神经元器件的识别精度

Figure 8. Recognition rate obtained from the neural network with different materials.

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图 9 输入磁场为0—15 mT时, 不同材料神经元器件的识别精度

Figure 9. When the magnetic field is 0–15 mT, the recognition rate obtained from the neural network with different materials.

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表 1 材料参数表

Table 1. Parameters of different materials.

参数	Terfenol-D	FeGa	Ni
杨氏模量Y/(10¹⁰ Pa)	8.0	2.5	21.4
磁致伸缩系数λ_s/10^–4	6.0	4.0	–0.2
吉尔伯特阻尼系数α	0.100	0.100	0.045
回磁比γ/(10⁵rad·s^–1·T^–1)	2.21	2.21	2.21
饱和磁化率M_s/(10⁵A·m^–1)	8.00	13.20	4.84
交换作用常数A/(10^–11 J·m^–1)	0.90	1.60	1.05

DownLoad: CSV

[1]	Aleksander I 2004 Nature 432 18
[2]	Linares-Barranco B, Sanchez-Sinencio E, Rodriguez-Vazquez A, Huertas J L 1991 IEEE J. Solid-State Circuits 26 956 Google Scholar
[3]	Lont J B, Guggenbuhl W 1992 IEEE Trans. Neural Networks 3 457 Google Scholar
[4]	陈怡然, 李海, 陈逸中, 陈凡, 李思成, 刘晨晨, 闻武杰, 吴春鹏, 燕博南 2018 人工智能 2 46 Google Scholar Chen Y R, Li H, Chen Y Z, Chen F, Li S C, Liu C C, Wen W J, Wu C P, Yan B N 2018 AI-View 2 46 Google Scholar
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[7]	刘东青, 程海峰, 朱玄, 王楠楠, 张朝阳 2014 物理学报 63 187301 Google Scholar Liu D Q, Cheng H F, Zhu X, Wang N N, Zhang C Y 2014 Acta Phys. Sin. 63 187301 Google Scholar
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[9]	Cai J L, Fang B, Zhang L, Lv W X, Zhang B S, Zhou T J, Finocchio G, Zeng Z M 2019 Phys. Rev. Appl. 11 034015 Google Scholar
[10]	Zhu J D, Zhang T, Yang Y C, Huang R 2020 Appl. Phys. Rev. 7 011312 Google Scholar
[11]	Yue K, Liu Y Z, Lake R K, Parker A C 2019 Sci. Adv. 5 eaau8170 Google Scholar
[12]	Fukami S, Ohno H 2018 J. Appl. Phys. 124 151904 Google Scholar
[13]	Sengupta A, Choday S H, Y Kim, Roy K 2015 Appl. Phys. Lett. 106 143701 Google Scholar
[14]	Fulara H, Zahedinejad M, Khymyn R, Dvornik M, Fukami S, Kanai S, Ohno H, Akerman J 2020 Nat. Commun. 11 4006 Google Scholar
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[43]	Vacca M, Graziano M, Crescenzo L D, Chiolerio A, Lamberti A, Balma D, Canavese G, Celegato F, Enrico E, Tiberto P, Boarino L, Zamboni M 2014 IEEE Trans. Nanotechnol. 13 963 Google Scholar
[44]	Liu J H, Yang X K, Zhang M L, Wei B, Li C, Dong D N, Li C 2018 IEEE Electron Device Lett. 40 220 Google Scholar
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