基于二维层状材料的神经形态器件研究进展

李策; 杨栋梁; 孙林锋

doi:10.7498/aps.71.20221424

摘要

近年来, 人工智能的发展对计算和存储的需求不断提升. 但是, 摩尔定律的放缓以及传统冯·诺依曼架构中计算与存储单元的分离, 导致了大量数据在搬运过程中功耗增加和时间延迟, 致使集成电路以及芯片设计面临越来越多的挑战. 这迫切需要开发新型计算范式来应对这种挑战. 而基于存算一体架构的神经形态器件, 可利用欧姆定律和基尔霍夫定律实现原位计算, 从而有望克服传统冯·诺依曼架构瓶颈. 通过调节具有“记忆”功能的忆阻器阻值, 实现类似生物大脑的人工神经网络, 并对复杂网络信号进行处理, 例如图像识别、模式分类和决策执行等. 二维材料由于其层状超薄特性和新奇的物理效应, 为进一步缩小器件尺寸并实现感存算一体提供了方案. 本文综述了基于二维材料的神经形态器件中的物理效应和忆阻特性, 并详细阐述了神经形态器件对LIF (leaky integrate and fire)模型、Hodgkin-Huxley模型等神经元模型以及长期可塑性、短期可塑性、放电时间依赖可塑性和尖峰频率依赖可塑性的模拟. 在此基础上, 进一步介绍了基于二维材料的神经形态器件在视觉、听觉以及触觉等领域的探索性应用. 最后本文总结了当前研究领域面临的问题以及对未来应用前景的展望.

关键词:

Abstract

In recent years, the development of artificial intelligence has increased the demand for computing and storage. However, the slowing down of Moore’s law and the separation between computing and storage units in traditional von Neumann architectures result in the increase of power consumption and time delays in the transport of abundant data, raising more and more challenges for integrated circuit and chip design. It is urgent for us to develop new computing paradigms to meet this challenge. The neuromorphic devices based on the in-memory computing architecture can overcome the traditional von Neumann architecture by Ohm’s law and Kirchhoff’s current law. By adjusting the resistance value of the memristor, the artificial neural network which can mimic the biological brain will be realized, and complex signal processing such as image recognition, pattern classification and decision determining can be carried out. In order to further reduce the size of device and realize the integration of sensing, memory and computing, two-dimensional materials can provide a potential solution due to their ultrathin thickness and rich physical effects. In this paper, we review the physical effects and memristive properties of neuromorphic devices based on two-dimensional materials, and describe the synaptic plasticity of neuromorphic devices based on leaky integrate and fire model and Hodgkin-Huxley model in detail, including long-term synaptic plasticity, short-term synaptic plasticity, spiking-time-dependent plasticity and spiking-rate-dependent plasticity. Moreover, the potential applications of two-dimensional materials based neuromorphic devices in the fields of vision, audition and tactile are introduced. Finally, we summarize the current issues on two-dimensional materials based neuromorphic computing and give the prospects for their future applications.

Keywords:

作者及机构信息

北京理工大学物理学院, 先进光电量子结构设计与测量教育部重点实验室, 北京 100081

通信作者: 杨栋梁, yangdl@bit.edu.cn ; 孙林锋, sunlinfeng@bit.edu.cn

基金项目: 北京市自然科学基金重点研究专题项目(批准号: Z210006)和国家自然科学基金青年科学基金(批准号: 12104051)资助的课题.

Authors and contacts

Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China

Corresponding author: Yang Dong-Liang, yangdl@bit.edu.cn ; Sun Lin-Feng, sunlinfeng@bit.edu.cn

Funds: Project Supported by the Key Research Program of Beijing Natural Science Foundation, China (Grant No. Z210006) and the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 12104051).

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施引文献

图 1 基于2D材料的忆阻器工作机制示意图, 其中包括经典效应, 如相变效应、铁电效应, 以及量子效应, 如导电细丝效应、氧空位效应、电荷捕获效应、隧穿效应、vdWs效应等

Fig. 1. Schematic diagram of physical working mechanisms of memristor based on 2D materials, including classical effects, such as phase change effect, ferroelectric effect, and quantum effect, like conductive filament effect, oxygen vacancy effect, charge trapping effect, tunneling effect, vdWs effect, etc.

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图 2 相变效应　(a) Au/MoS₂/Au器件结构示意图以及在电场作用下Li⁺调控MoS₂发生可逆相变过程的示意图^[40]; (b) Au/MoS₂/Au器件的I-V特性曲线^[40]; (c) 通过脉冲编程电压改变电导增量^[40]; (d) MoTe₂在电场作用下的相变过程以及MoTe₂在2H (左下)和2H_d(右下)态下的STEM图像^[41]; (e) 电形成过程前后的I-V扫描曲线^[41]

Fig. 2. Phase change effect: (a) Schematic diagram of Au/MoS₂/Au device structure and Li⁺ regulating the reversible phase change process of MoS₂ under the applied electric field^[40]; (b) typical I-V curve of Au/MoS₂/Au device^[40]; (c) conductance changing with continuous pulse programming voltage^[40]; (d) phase change process of MoTe₂ with electric field applied and the STEM images of 2H (bottom left) and 2H_d (bottom right) states of MoTe₂^[41]; (e) I-V curves of devices before and after forming processes^[41].

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图 3 铁电效应　(a) 基于α-In₂Se₃的FeSFET器件示意图^[43]; (b) α-In₂Se₃沟道材料向上和向下极化时的状态图示, 以及相应的能带图^[43]; (c) 不同V_g扫描下器件传输特性曲线^[43]; (d) 器件在5个连续的周期脉冲电压下稳定突触后电流(PSC)的增强、抑制效果图^[43]

Fig. 3. Ferroelectric effect: (a) Schematic of the α-In₂Se₃ based FeSFET^[43]; (b) illustrations of the upward and downward polarized states of α-In₂Se₃ channel material and the corresponding energy band diagram^[44]; (c) device transfer characteristic curves under different scanning ranges of V_g^[43]; (d) the potentiation and depression process of the post-synaptic-current (PSC) under 5 continuous periodic voltage pulses^[43].

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图 4 导电细丝效应　(a) Ag/SnO_x/SnSe器件示意图以及器件的横截面TEM图像^[78]; (b) 忆阻器的保持性超过10⁵ s^[78]; (c) Ag/SnO_x/SnSe器件初始状态、导电细丝形成和断裂的示意图^[78]; 在CDG (d)和DDG (e)器件中导电细丝形成和断裂示意图^[83]

Fig. 4. Conductive filament effect: (a) Schematic of Ag/SnO_x/SnSe device and the cross-sectional TEM image of the interface^[78]; (b) the retention of the device over 10⁵ s^[78]; (c) schematic of Ag/SnO_x/SnSe device at initial state, conductive filament formation process and fracture state^[78]; schematic of conductive filament formation and rupture in CDG (d) and DDG (e) device^[83].

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图 5 氧空位效应　(a) Ti/HfSe_xO_y/HfSe₂/Au忆阻器示意图^[50]; (b) 器件的电学特性: 在低工作电流(100 nA)下, 器件的I-V曲线^[50]; (c) 锥形氧空位通道在电压调控下形成和断裂的过程^[50]; (d) Pd/WS₂/Pt器件结构示意图^[49]; (e) 文献中报道的不同的编程或SET电流的比较^[49]

Fig. 5. Oxygen vacancy effect: (a) Schematic of Ti/HfSe_xO_y/HfSe₂/Au memristor; (b) electrical characteristics of the device: I-V curves of the device at low operating current (100 nA) ^[50]; (c) the formation and rupture of conical oxygen vacancy channels under voltage regulation^[50]; (d) schematic of the Pd/WS₂/Pt device^[49]; (e) comparison of various programs or SET currents reported in the literatures^[49].

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图 6 隧穿效应　(a) 基于MoS₂的多端器件的示意图^[55]; (b) 浮栅/h-BN/漏极的能带图^[55]; (c) 基于MoS₂的多端器件在V_ds = 1 V的传输特性^[55]; (d) 在不同V_ds下的开关行为^[55]; (e) 基于三端器件的突触示意图^[55]; (f) 不同V_g下多端突触器件重复性增强和抑制行为的对数图^[55]

Fig. 6. Tunneling effect: (a) Schematic diagram of the MoS₂-based multi-terminal device^[55]; (b) band diagram of floating-gate/h-BN/drain^[55]; (c) transmission characteristics of multi-terminal device based MoS₂ at V_ds = 1 V^[55]; (d) switching behavior at different V_ds^[55]; (e) schematic diagram of a synapse based on a three-terminal device^[55]; (f) logarithmic plots of repetitive potentiation and inhibitory behavior of multiterminal synaptic apparatus under different V_g^[55].

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图 7 电荷的捕获与释放　(a) 基于2D MoTe₂的忆阻器结构示意图^[53]; (b) 在V_g(–40 V→40 V→–40 V)扫描电压下器件的传输特性曲线(插图为在对数坐标下的I-V曲线)^[53]; (c) 2D MoTe₂的忆阻器工作机制示意图^[53]; (d) 生物突触和基于sr-SiN_x的人工突触器件的示意图; (e) 100个周期内增强(左)和抑制(右)周期性电导的变化^[53]

Fig. 7. Charge trapping and de-trapping effects: (a) Schematic diagram of the memristor structure based on 2D MoTe₂^[53]; (b) the transfer characteristic curve of the device under the scanning voltage of V_g (–40 V→40 V→–40 V) (the illustration is the same curve shown in logarithmic coordinates) ^[53]; (c) the working mechanism of the device^[53]; (d) schematic illustration of biological synapses and sr-SiN_x-based artificial synaptic device^[53]; (e) the conductance periodic changes in excitation (left) and inhibition (right) over 100 cycles^[53].

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图 8 横向vdWs异质结　(a) 基于2D WSe₂-WO₃横向异质结构的器件示意图^[129]; (b) 由Gate 1调节的电阻开关特性^[129]; (c) WSe₂-WO₃横向异质结构的光学图像^[129]; (d) Pd-WSe₂-Pd(电极4和5)、Pd-WO₃-Pd(电极1和2)和Pd-WSe₂-WO₃-Pd(电极3和4)的I-V特性曲线^[129]; (e), (f) 开关原理的示意图, 其中红色圆圈代表质子^[129]

Fig. 8. Lateral vdWs heterostructure: (a) Schematic diagram of the device based on the 2D WSe₂-WO₃ lateral heterostructure^[129]; (b) resistive switching characteristics regulated by Gate 1 voltage^[129]; (c) optical image of WSe₂-WO₃ lateral heterostructure^[129]; (d) I-V characteristic curves of Pd-WSe₂-Pd (electrodes 4 and 5), Pd-WO₃-Pd (electrodes 1 and 2) and Pd-WSe₂-WO₃-Pd (electrodes 3 and 4) ^[129]; (e), (f) schematic of the switching principle, where the red circles represent protons^[129].

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图 9 垂直vdWs异质结　(a) 基于MoS_2–xO_x/Gr异质结的器件示意图^[130]; (b) 器件在不同温度下的开关曲线^[130]; (c) 器件在340和160 ℃下的保持时间^[130]

Fig. 9. Vertical vdWs heterojunction: (a) Schematic diagram of the device based on MoS_2–xO_x/Gr heterojunction^[130]; (b) switching curves of the device at different temperatures^[130]; (c) retention time of device at 340 and 160 ℃ ^[130].

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图 10 LIF模型神经元　(a) 平面器件Ag/MoS₂/TiW 示意图^[139]; (b) 器件在连续脉冲序列下的泄漏-集成-发射的电学行为^[139]; (c) 器件在1 ms、电压为2.0 V的单脉冲下的易失性开关行为^[139]; (d) 垂直器件Ag/MoS₂/Au的结构示意图和光学图片^[140]; (e) 上图为Ag/MoS₂/Au人工神经元的电路图, 下图为神经元的连续输出电流尖峰^[140]; (f) 上图为电路图节点B处的电压V_B, 下图为负载电阻R_L 两端的电压V_RL^[140]

Fig. 10. LIF model neurons: (a) Schematic diagram of planar device Ag/MoS₂/TiW^[139]; (b) leakage-integration-emission electrical behavior of device under continuous pulse trains^[139]; (c) volatile switching behavior of the device with a single pulse of 2.0 V at 1 ms^[139]; (d) schematic diagram and optical picture of the vertical device Ag/MoS₂/Au^[140]; (e) the top picture is the circuit diagram of the Ag/MoS₂/Au artificial neuron, and the picture below is the continuous output current spike of the neuron^[140]; (f) the picture in the top panel shows the voltage V_B at node B of the circuit diagram, and the picture in the bottom panel shows the voltage V_RL across the load resistance R_L^[140].

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图 11 H-H尖峰神经元　(a) 器件的光学图片示意图^[141]; (b) 器件结构示意图^[141]; (c) H-H模型神经元的电路等效图^[141]; (d) H-H模型中g_K的时间演变关系图^[141]; (e) H-H 模型中g_Na的时间演变关系图^[141]; (f) GHeT神经元的完整电路图^[141]; (g) 图(d)中的 GHeT神经元电路的前30 s的实验结果^[141]

Fig. 11. H-H spiking neurons: (a) Optical image of the device^[141]; (b) the schematic of device structure^[141]; (c) equivalent circuit diagram of H-H model neuron^[141]; (d) time evolution diagram of g_K in H-H model^[141]; (e) time evolution diagram of g_Na in H-H model^[141]; (f) complete circuit diagram of a GHeT neuron^[141]; (g) experimental results for the first 30 s of the GHeT neuron circuit in panel (d)^[141]

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图 12 LTSP　(a) 人工突触器件示意图^[42]; (b) 重复进行LTP和LTD操作, 一个周期是100个增强脉冲和随后的100个抑制脉冲. 左上图和右上图分别对应最初10个循环和最后10个循环的运行情况^[42]; (c) 离子门控突触晶体管的示意图^[149]; (d) 生物系统(上)和离子门控突触晶体管(下)中离子迁移和动态平衡过程^[149]; (e) 一系列电压脉冲(5 V, 50 ms)施加到栅极(V_ds = 0.5 V)时, 实现从STSP到LTSP的转换^[149]; (f) 对基于WSe₂的突触晶体管使用增强(1.2 V, 100 ms)和抑制(–0.4 V, 100 ms)脉冲信号, 间隔3 s, 显示出良好的线性、对称性和重复性^[149]

Fig. 12. LTSP: (a) Schematic diagram of artificial synapse^[42]; (b) the LTP and LTD operations were repeated with a cycle of 100 enhancement pulses followed by 100 inhibition pulses; the upper left and upper right diagrams correspond to the operation of the first 10 cycles and the last 10 cycles, respectively^[42]; (c) schematic of an ion-gated synaptic transistor^[149]; (d) ion migration and dynamic equilibrium in biological systems (top) and ion-gated synaptic transistors (bottom)^[149]; (e) when a series of voltage pulses (5 V, 50 ms) are applied to the gate (V_ds = 0.5 V), the transition occurs from STSP to LTSP^[149]; (f) by using excitatory (1.2 V, 100 ms) and inhibitory (–0.4 V, 100 ms) pulsed signals with 3 s intervals for WSe₂-based synaptic transistors, the device shows good linearity, symmetry, and reproducibility^[149].

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图 13 STSP　(a) 生物突触示意图; (b) PPD指数和 PPF指数与Δt的函数关系图^[26]; (c) IPSC 和 (d) EPSC效果示意图

Fig. 13. STSP: (a) Schematic diagram of a biological synapse; (b) PPD index and PPF index are shown as the function of Δt^[26]; (c) IPSC and (d) EPSC effect diagram.

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图 14 STDP和SRDP　(a) 四种STDP模型突触权重变化的示意图; (b) 典型的STDP模型. 突触前电流(兴奋性)和IPSC的相对时间前后影响突触权重产生LTP和LTD; (c) 典型的SRDP模型, 尖峰频率的大小带来的突触权重的变化

Fig. 14. STDP and SRDP: (a) Schematic diagram of synaptic weight changes of the four STDP models; (b) typical STDP model. Influence synaptic weights to generate LTP and LTD on relative timing of presynaptic currents (excitatory) and IPSCs; (c) typical SRDP model; changes of synaptic weights caused by the magnitude of the spike frequency.

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图 15 视觉识别　(a) 人体视觉系统的示意图^[194]; (b) 基于ORRAM阵列的人工神经形态视觉系统, 以及用于图像识别的人工神经网络示意图^[194]; (c) 人工神经形态视觉系统预处理之前(左)和之后(右)的图像示例^[194]; (d) 有/无图像预处理的图像识别率比较^[194]; (e) 在生物RC系统上执行的认知任务示意图^[195]; (f) 由电和光输入刺激的多功能忆阻器阵列示意图; (g) 对语言符号的识别准确率^[195]

Fig. 15. Visual recognition: (a) Schematic diagram of the human visual system^[194]; (b) artificial neuromorphic vision system based on ORRAM array, and artificial neural network for image recognition^[194]; (c) images before (left) and after (right) preprocessing through an artificial neuromorphic vision system^[194]; (d) comparison of image recognition rates with and without image preprocessing^[194]; (e) schematic diagram of cognitive tasks performed on biological RC systems^[195]; (f) illustration of a multifunctional memristor array stimulated by various electrical and optical inputs; (g) recognition accuracy of language sign^[195].

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图 16 声音定位与模式识别　(a) ITD和ILD的声音定位示意图^[26]; (b) 电压扫描下焦耳热驱动的电导变化; (c) 两个连续脉冲刺激后的PPF和PPD指数与Δt的函数关系图^[26]; (d) 基于ITD的声音定位的突触计算工作机制示意图; “CA”指图(a)所示的耳蜗, 蓝色的圆圈代表神经元, 水平虚线代表神经元放电的潜在阈值^[26]; (e) 生物突触与vdWs混合突触器件的功能和结构比较^[167]; (f) 基于三种人工神经网络的声学模式识别率, 并与SW-NN的识别率进行比较^[167]

Fig. 16. Sound localization and pattern recognition: (a) Schematic diagram of sound localization of ITD and ILD^[26]; (b) joule heat driven conductivity change under the sweep voltage^[26]; (c) the pulse intervals dependent PPF and PPD indexes stimulated by two consecutive pulses^[26]; (d) schematic diagram of the synaptic computing of ITD-based sound localization; “CA” refers to the cochlea shown in panel (a); the blue circles represent neurons; horizontal dashed lines represent potential thresholds for neuronal firing^[26]; (e) comparison of function and structure between biological synapses and vdWs hybrid synaptic devices^[167]; (f) acoustic pattern recognition rates based on three artificial neural networks with compared results achieved by the SW-NN recognition rate^[167].

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图 17 触觉模拟　(a) 生物触觉神经系统^[202]; (b) 带有离子凝胶门控晶体管的 PENG 示意图^[202]; (c) 压电Gr人工触觉突触工作原理^[202]; (d) PSC幅度与应变脉冲数的关系图^[202]

Fig. 17. Tactile mimicking: (a) Biological sensory nervous system^[202]; (b) schematic diagram of a PENG with iongel-gated transistors^[202]; (c) the working principle of piezoelectric Gr artificial sensory synapse^[202]; (d) PSC amplitude shown as the function of strain-pulse number^[202].

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表 1 不同材料的人工突触器件性能对比

Table 1. Performance comparison of artificial synaptic devices based on different materials.

	材料	突触可塑性	耐久性 (循环)	保持性	功耗/能耗	刺激方式	文献
2D材料	MoS₂	LTP, LTD, PPF, SRDP	100	>11 h	4.5 fJ	电	[166]
	h-BN/WSe₂	LTP, LTD, STDP	—	—	66 fJ	光电	[167]
	SnSe	LTP, LTD, PPF, STDP	230	>10⁴ s	66 fJ	电	[44]
	MoTe₂	LTP, LTD, PPF, STDP	570	>10⁴ s	—	电	[53]
氧化物材料	TiO₂	LTP, LTD, PPF, STDP	—	—	26 pJ	电	[168]
	ZTO	LTP, LTD, STDP	—	10⁴ s	—	电	[169]
	IGZO	LTP, LTD	—	—	160 pJ	光电	[170]
	In₂O₃	LTP, LTD, PPF, SRDP	20	—	—	电	[171]
有机材料	C₈-BTBT	LTP, LTD	10⁵	>3500 s	<5 fJ	电	[177]
	PMMA	LTP, LTD, PPF, SRDP, STDP	—	>10³ s	10^-8 W	电	[178]
	PVA	PPF, LTP, LTD, LTM	—	600 s	—	光	[179]
	P(VDF-TrFE) /P(VP-EDMAEMAES)	LTP, LTD, PPF	—	—	75 pJ	电	[180]
钙钛矿材料	BaTiO₃	STDP	10⁵	10⁸ s	600 pJ	电	[182]
	PdZr_0.52Ti_0.48O₃	SRTP, LSTP	—	—	2.5 pJ	电	[183]
	BiFeO₃	STDP	—	—	200 nW	电	[184]
	(CH₃NH₃)₃Sb₂Br₉	LTP, LTD, STDP	300	10⁴ s	117.9 fJ	电	[185]

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