氧化物神经元器件及其神经网络应用

doi:10.15541/jim20230405

无机材料学报 ›› 2024, Vol. 39 ›› Issue (4): 345-358.DOI: 10.15541/jim20230405

• 综述 • 下一篇

氧化物神经元器件及其神经网络应用

李宗晓¹(), 胡令祥¹, 王敬蕊², 诸葛飞¹^,³^,⁴^,⁵()

1.中国科学院宁波材料技术与工程研究所, 宁波 315201
2.宁波工程学院电子与信息工程学院, 宁波 315211
3.中国科学院脑科学与智能技术卓越创新中心, 上海 200031
4.中国科学院大学材料与光电研究中心, 北京 100029
5.浙江大学温州研究院, 温州 325006

收稿日期:2023-09-05 修回日期:2023-11-28 出版日期:2024-04-20 网络出版日期:2023-12-19
通讯作者: 诸葛飞, 研究员. E-mail: zhugefei@nimte.ac.cn
作者简介:李宗晓(1986-), 男, 博士. E-mail: lizongxiao@nimte.ac.cn
基金资助:
国家自然科学基金(U20A20209);中国科学院战略性先导专项(XDB32050204);中国博士后创新人才支持计划(BX2021326);中国博士后科学基金(2021M703310);浙江省自然科学基金(LQ22F040003);宁波市自然科学基金(2021J139);宁波市自然科学基金(2023J356);环境友好能源材料国家重点实验室开放基金(20kfhg09)

Oxide Neuron Devices and Their Applications in Artificial Neural Networks

LI Zongxiao¹(), HU Lingxiang¹, WANG Jingrui², ZHUGE Fei¹^,³^,⁴^,⁵()

1. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
2. School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
3. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
4. Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100029, China
5. Institute of Wenzhou, Zhejiang University, Wenzhou 325006, China

Received:2023-09-05 Revised:2023-11-28 Published:2024-04-20 Online:2023-12-19
Contact: ZHUGE Fei, professor. E-mail: zhugefei@nimte.ac.cn
About author:LI Zongxiao (1986-), male, PhD. E-mail: lizongxiao@nimte.ac.cn
Supported by:
National Natural Science Foundation of China(U20A20209);Strategic Priority Research Program of Chinese Academy of Sciences(XDB32050204);China National Postdoctoral Program for Innovative Talents(BX2021326);China Postdoctoral Science Foundation(2021M703310);Zhejiang Provincial Natural Science Foundation(LQ22F040003);Ningbo Natural Science Foundation(2021J139);Ningbo Natural Science Foundation(2023J356);State Key Laboratory for Environment-Friendly Energy Materials(20kfhg09)

摘要/Abstract

摘要：

目前, 人工智能在人类社会发挥着越来越重要的作用, 以深度学习为代表的人工智能算法对硬件算力的要求也越来越高。然而随着摩尔定律逼近极限, 传统冯·诺依曼计算架构越来越难以满足硬件算力提升的迫切需求。受人脑启发的新型神经形态计算采用数据处理与存储一体架构, 有望为开发低能耗、高算力的新型人工智能技术提供重要的硬件基础。人工神经元和人工突触作为神经形态计算系统的核心组成部分, 是当前研究的前沿和热点。本文聚焦氧化物人工神经元, 从神经元数学模型出发, 重点介绍了基于氧化物电子器件的霍奇金-赫胥黎神经元、泄漏-累积-发射神经元和振荡神经元的最新研究进展, 系统分析了器件结构、工作机制对神经元功能模拟的影响规律。进一步, 根据不同尖峰发射动态行为, 阐述了基于氧化物神经元硬件的脉冲神经网络和振荡神经网络的研究进展。最后, 讨论了氧化物神经元在器件、阵列、神经网络等层面面临的挑战, 并展望了其在神经形态计算等领域的发展前景。

关键词: 氧化物, 神经元器件, 类脑计算, 神经形态计算, 人工神经网络, 综述

Abstract:

Nowadays, artificial intelligence (AI) is playing an increasingly important role in human society. Running AI algorithms represented by deep learning places great demands on computational power of hardware. However, with Moore's Law approaching physical limitations, the traditional Von Neumann computing architecture cannot meet the urgent demand for promoting hardware computational power. The brain-inspired neuromorphic computing (NC) employing an integrated processing-memory architecture is expected to provide an important hardware basis for developing novel AI technologies with low energy consumption and high computational power. Under this conception, artificial neurons and synapses, as the core components of NC systems, have become a research hotspot. This paper aims to provide a comprehensive review on the development of oxide neuron devices. Firstly, several mathematical models of neurons are described. Then, recent progress of Hodgkin-Huxley neurons, leaky integrate-and-fire neurons and oscillatory neurons based on oxide electronic devices is introduced in detail. The effects of device structures and working mechanisms on neuronal performance are systematically analyzed. Next, the hardware implementation of spiking neural networks and oscillatory neural networks based on oxide artificial neurons is demonstrated. Finally, the challenges of oxide neuron devices, arrays and networks, as well as prospect for their applications are pointed out.

Key words: oxide, neuron device, brain-inspired computing, neuromorphic computing, artificial neural network, review

中图分类号:

O472

李宗晓, 胡令祥, 王敬蕊, 诸葛飞. 氧化物神经元器件及其神经网络应用[J]. 无机材料学报, 2024, 39(4): 345-358.

LI Zongxiao, HU Lingxiang, WANG Jingrui, ZHUGE Fei. Oxide Neuron Devices and Their Applications in Artificial Neural Networks[J]. Journal of Inorganic Materials, 2024, 39(4): 345-358.

图/表 9

图1 生物神经元结构及其动态响应

Fig. 1 Typical structure and dynamic response of biological neuron (a) Typical structure of biological neuron; (b) Schematic diagram of voltage-gated ion channels in neural membrane; (c) Dynamic changes of membrane potential under stimulation

图2 基于VO2忆阻器的HH神经元[32]

Fig. 2 Hodgkin-Huxley (HH) neuron based on VO2 memristors[32] (a) HH neuron circuit based on VO2-based memristors; (b) Output resonator spiking of VO2-based HH neuron; (c) Output inhibition-induced spiking of VO2-based HH neuron; (d) Spike frequency adaptation property of HH neuron

图3 基于反铁电场效应晶体管的LIF神经元[43]

Fig. 3 Leaky integrate-and-fire (LIF) neuron based on antiferroelectric field effect transistor[43] (a) Volatile typical transfer curves of an antiferroelectric field effect transistor (AFeFET) (up) and continuous firing events of an AFeFET neuron under voltage pulses (down); (b) Dynamic of leaky and integration process of an AFeFET neuron under gate pulse with different amplitudes; (c) Artificial neuron circuit diagram based on AFeFET

图4 基于反铁磁材料的LIF神经元[51]

Fig. 4 LIF neuron based on an antiferromagnetic spintronic device[51] (a) Schematic of an antiferromagnetic spintronic device; (b) Schematic of a polar magneto-optic Kerr effect microscope setup for in-situ magneto-electrical transport probing (up) and its measured domain wall position of hall bar under current stimuli (down); (c) Domain wall position signal (up), neural threshold signal (middle) and output voltage spike dynamics (down) of the antiferromagnetic spintronic device under current stimuli with inset presenting dynamics of domain wall motion

图5 氧化物忆阻器基LIF神经元研究

Fig. 5 Researches of LIF neurons based on oxide memristors (a) Typical I-V curve of a NbOx-based TS memristor[63]; (b) Schematic diagram of artificial spiking neuron circuit based on NbOx-based memristor[63]; (c) Oscillation and output spiking characteristics of memristive neuron under constant voltage stimuli[63]; (d) Schematic illustration of an optoelectronic neuron with ITO/IGZO/Ag/Ta2O5/ITO structure[73]; (e) Comparison of fire dynamics of the optoelectronic neuron under dark and ultraviolet light[73]; (f) Fire frequency of the optoelectronic neuron as a function of light intensity at different wavelengths[73]

图6 振荡神经元耦合电路及其振荡波同步输出图

Fig. 6 Coupling circuits of oscillation neurons and the output mutual waves (a) Circuit schematic of three spin-torque nano-oscillators connected electrically[108]; (b) Unsynchronized oscillatory wave of the three coupled oscillators[108]; (c) Synchronized oscillatory wave of the three coupled oscillators[108]; (d) Coupled circuit consisting of two VO2-based oscillators[96]; (e) In-phase voltage oscillatory waves of VO2-based coupled circuit[96]; (f) Out-of-phase voltage oscillatory waves of VO2-based coupled circuit[96]

表1 氧化物基HH、LIF和振荡神经元性能对比

Table 1 Performance comparison of HH, LIF and oscillation neurons based on oxides

Type	Device structure	Physics	Auxiliary circuit	Operation stimulus	Highest output frequency	Energy consumption per spike	Advanced function	Ref.
HH	Pt/VO₂/Pt	Mott	2S2R2C* 2S1R3C* 2S2R3C*	Current/ Voltage	<60 kHz	5.6 fJ	23 types of biological neuronal behaviors	[32]
HH	W/WO₃/PEDOT:PSS/Pt	Proton migration	CMOS	2 V	—	—	Local graded potential, all or nothing	[34]
LIF	Si:HfO₂-based FeFET	Polarization switching	6T*	2.4 V	—	—	Integration of excitatory and inhibitory inputs	[42]
	Hf_0.5Zr_0.5O₂-based FeFET	Polarization switching	5T1C*	1.8 V	—	—	Spike frequency adaptation	[44]
	Hf_0.2Zr_0.8O₂-based FeFET	Polarization switching	6T1R*	1.8 V	—	37 fJ	Adjustable output frequency	[43]
	MTJ	Spin	1T*	—	17 MHz	486 fJ	Adjustable output frequency	[51]
	Pt/Ag/TiN/HfAlO_x/Pt	Filament	2R1C*	1.5 V	—	16 fJ	Adjustable output frequency	[111]
	Ag/SiO₂/SiO_2.03/Pt	Filament	2R1C*	0.1 V	—	2 fJ	Adjustable output frequency	[79]
	Au/VO₂/Au	Mott	2R1C*	5 V	1 MHz	2.9 nJ	Adjustable output frequency	[112]
	Si/NbO₂/TiN	Mott	1R*	2 V	900 kHz	38 pJ	Self-protection	[110]
Oscillation	Pt/TaO_x/Ta/Pt	Filament	1R1C*	4-6V	250 MHz	300 μW	Adjustable output frequency	[105]
	Ag/HfO_x/Pt	Filament	1R*	0.6 V	~80 kHz	1.8 µW	Adjustable output frequency	[113]
	Pt/NbO_x/Pt	Mott	1R1C*	4 V	33 MHz	—	Adjustable output frequency	[94]
	VO₂	Mott	1R1C*	2.5 V	1 MHz	735 mW	Coupling	[114]

图7 基于氧化物神经元硬件的SNN研究

Fig. 7 Researches on hardware implementations of spiking neural network (SNN) based on oxide neurons (a) Schematic of an SNN with 8×3 array network for unsupervised learning[116]; (b) Evolution of input voltages, neuron currents and synaptic weights in the unsupervised learning process[116]; (c) Neuromorphic circuit based on hybrid memristor/CMOS neurons[119]; (d) Circuit diagram of the V/VOx/HWOx/Pt-based SNN hardware system[120]; (e) Output spike frequency (Vout) as a function of resistance of R-mode device[120]

图8 基于VO2振荡神经网络的伊辛解算器[121]

Fig. 8 VO2 oscillator-based oscillatory neural network (ONN) for Ising Hamiltonian solver[121] (a) Eight-node Ising model; (b) Schematic of a phase-transition nano-oscillator consisting of a VO2-based memristor in series with a transistor (left) and the scanning electron microscopy image of the VO2-based memristor (right); (c) Measured oscillatory waveforms in no-synchronization state, first-harmonic injection-locking and second-harmonic injection-locking

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氧化物神经元器件及其神经网络应用

Oxide Neuron Devices and Their Applications in Artificial Neural Networks

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