Multimode modulated memristors for in-sensor computing system

Zhang Yu-Qi; Wang Jun-Jie; Lü Zi-Yu; Han Su-Ting

doi:10.7498/aps.71.20220226

Abstract

To develop future interactive artificial intelligence system, the construction of high-performance human perception system and processing system is vital. In a traditional perceptual and processing system, sensors, memory and processing units are physically separated because of their different functions and manufacture conditions, which results in frequent shuttling and format transformation of data resulting in long time delay and high energy consumption. Inspired by biological sensory nervous system, one has proposed the concept of in-sensor computing system in which the basic unit integrates sensor, storage and computing functions in the same place. In-sensor computing technology can provide a reliable technical scheme for the area of sensory processing. Artificial memristive synapse capable of sensing light, pressure, chemical substances, etc. is one type of ideal device for the application of in-sensor computing system. In this paper, at the device level, recent progress of sensory memristive synapses applied to in-sensor computing systems are reviewed, including visual, olfactory, auditory, tactile and multimode sensation. This review points out the challenge and prospect from the aspects of device, fabrication, integrated circuit system architecture and algorithms, aiming to provide possible research direction for future development of in-sensor computing system.

Keywords:

Authors and contacts

1.
Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, China
2.
College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China

Corresponding author: Han Su-Ting, sutinghan@szu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62122055, 62074104, 61974093).

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Cited By

图 1 (a) 传统的感知处理系统架构; (b) 人体五感示意图; (c) 感存算一体化系统架构; (d) 低级感官处理功能; (e) 用于神经网络计算的可重构响应度的感存算一体单元阵列; (f)感存算一体化技术的应用领域

Figure 1. (a) Traditional architecture of sensing and processing; (b) schematic of human sensory system; (c) in-sensor computing architecture; (d) low-level sensory processing functions; (e) in-sensor computing units with reconfigurable responsivity for neural network computing; (f) application fields of in-sensor computing technology.

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图 2 (a) 两端忆阻器示意图; (b) 数字型忆阻器的典型电压-电流曲线; (c) 模拟型忆阻器的典型电压-电流曲线; (d) 忆阻器常见机理; (e) 数字型和模拟型忆阻器的应用

Figure 2. (a) Schematic of a two-terminal memristor; (b) typical I-V curve of digital memristor; (c) typical I-V curve of analog memristor; (d) three main mechanisms of memristors; (e) application of analog and digital memristor.

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图 3 (a) 人类视觉系统示意图; (b)突触、神经元和制备的忆阻器示意图; (c)大脑STP和LTP行为的示意图; (d) 人工突触在红光和紫外光刺激下电流响应对比图^[42]; (e) 可见光/紫外光调控突触可塑性示意图; (f) 人工突触在可见光脉冲刺激下的电流响应; (g) 人工突触在紫外光脉冲刺激下的电流响应; (h) 可见光调控的突触STDP功能模拟; (i) 基于忆阻器阵列的视觉感存算一体系统低级处理和高级处理功能示意图^[56]

Figure 3. (a) Schematic of the human visual system; (b) schematic diagrams of the synapse, neuron, and two-terminal memristor; (c) schematic diagram of STP and LTP behavior; (d) comparison of current response of artificial synapses under red light and ultraviolet light^[42]; (e) diagram of synaptic plasticity regulated by visible/ultraviolet light; (f) current response of artificial synapses stimulated by visible light pulses; (g) current response of artificial synapses stimulated by ultraviolet light pulses; (h) simulation of synaptic STDP function regulated by visible light; (i) schematic diagram of low-level and high-level processing functions of visual in-sensor computing system based on memristor array^[56].

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图 4 (a) 生物触觉感知系统示意图; (b) 压力传感器和Nafion忆阻器集成的人工触觉感知系统; (c) 触觉系统在不同按压力度下的电流响应图; (d) 对采集到的数据进行K邻近分类网络算法处理^[61]; (e) 集成触觉传感器和HfO₂基忆阻器的触觉感觉神经; (f) “SOS”和“TEAM”莫斯电码信号刺激人工触觉神经元的电流响应^[66]; (g) MXene传感器、ADC-LED电路、光电忆阻器构成的神经系统; (h) 光调控的突触PPF模拟^[64]

Figure 4. (a) Schematic illustration of the biological haptic perception system; (b) artificial haptic perception system consisting of pressure sensor and Nafion-based memristor; (c) current response of tactile system at different pressing magnitudes; (d) schematic of processing by K-nearest neighbors algorithm^[61]; (e) tactile sensory nerve consisting of haptic sensor and HfO₂-based memristor; (f) current response of artificial tactile neuron under “SOS” and “TEAM” Morse code signals stimulus^[66]; (g) artificial afferent nerve system integrating MXene sensor, ADC-LED circuit and optoelectronic memristor; (h) simulation of photo-tunable synaptic PPF behavior^[64].

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图 5 (a) 生物嗅觉感知系统示意图; (b) 人工嗅觉推理系统原理图; (c) W/WO₃/PEDOT:PSS/Pt忆阻器在脉冲下刺激下的电流相应; (d) 所用忆阻器突触真实和理想的电导调制曲线^[73]; (e) 气敏忆阻器机理示意图; (f) SnO₂气敏忆阻器对不同浓度一氧化氮气体的电流响应; (g) 由Ta₂O₅, HfO₂和SnO₂忆阻器组成的气体感知阵列^[71]

Figure 5. (a) Schematic of biological olfactory system; (b) schematic of artificial olfactory inference system; (c) current response of memristor with W/WO₃/PEDOT:PSS/Pt structure under pulse stimulus; (d) experimental and ideal conductance modulation curves of the memristive synapse^[73]; (e) schematic of the gas sensing mechanism; (f) current response of SnO₂ based gas-sensing memristor depending on NO gas concentration; (g) schematic diagram of the gas-sensing array consisting of Ta₂O₅, HfO₂, and SnO₂-based memristors^[71].

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图 6 (a)柔性MXene-ZnO忆阻器示意图; (b)器件在不同紫外光照强度下的I-V曲线; (c) MXene-ZnO忆阻器受光和湿度调控的电流分布图; (d) 应用光和电脉冲实现突触LTP和LTD行为的模拟; (e) 基于光和湿度调控的忆阻器突触搭建的神经网络示意图^[41]; (f) 多模脉冲感知处理系统工作流程图^[81]

Figure 6. (a) Schematic structure of the flexible MXene-ZnO-based memristive device; (b) I-V curves of device under UV irradiance with different intensities; (c) current profile of MXene-ZnO memristor regulated by light and humidity; (d) simulation of synaptic LTP and LTD behaviors by UV light and electrical pulses; (e) schematic of neural network based on MXene-ZnO-based memristive synapses^[41]; (f) operational diagram of the multimode spiking perception and processing system^[81].

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表 1 应用于感存算一体化系统的忆阻器的性能比较

Table 1. Performance comparison of memristors applied to in-sensor computing systems.

	忆阻器结构	响应类型	阻变机理	开启/关闭电压/V	开关比	PSC	STP	LTP	具体实现功能	文献
视觉	Ag/CH₃NH₃PbI₃ (OHP)/ITO	—	碘空位导电细丝	0.32/–0.52	1×10⁴	√	√	√	数字识别分类	[47]
	Ni/Al₂O₃/Au	UV	金属导电细丝	1.7/–1.6	1×10²	—	—	—	图像记忆	[38]
	Pd/MoO_x/ITO	UV	界面效应	–2.13	40	√	√	√	图像预处理	[39]
	Ag nanowire/TiO₂	visible light (vis)	界面效应	—	—	√	√	√	广角感知、处理存储	[50]
	glass/ITO/ZnO/PbS/ZnO/Al	UV/infrared ray (IR)	氧空位导电细丝	—	—	√	√	√	数字识别分类	[45]
	ITO/Nb:SrTiO₃	vis	界面效应	—	—	√	√	√	自适应光电突触	[48]
	ITO/PEDOT:PSS/CuSCN/CsPbBr₃ PNs/Au	UV	界面效应	—	—	√	√	√	回溯记忆功能的图像记忆	[51]
	ITO/SnO₂/CsPbCl₃/TAPC/TAPC:MoO₃/MoO₃/Ag/MoO₃	UV/red light	界面效应	—	—	√	√	√	双模式图像检测记忆	[42]
	RGO/GO-NCQD/graphene	UV	氧空位导电细丝	—	—	√	√	√	图像识别	[53]
	ITO/CsPbBr₂I/P₃HT/Ag	vis/NIR	卤素空位导电细丝	0.4/–0.4	> 10	√	√	√	图像预处理	[46]
	ITO/PCBM/MAPbI₃:Si NCs/Spiro-OMeTAD/Au	UV/NIR/vis	界面效应	—	—	√	√	—	图像预处理	[54]
	Au/Ag-TiO₂/FTO	vis/UV	表面等离子体共振效应/金属导电细丝	3.4/–1.8	1×10³	√	√	√	图像预处理及识别	[56]
	Ag/Cu₃P/ITO	λ = 660 nm	金属导电细丝	—	1×10⁴	√	√	√	回溯记忆功能的图像记忆	[57]
	Ni/p-NiO/n-ZnO/Ni	UV	界面效应	—	—	√	—	—	图像记忆	[40]
	ITO/MXene-ZnO/Al	UV	氧空位导电细丝	-0.5/1.2	1×10⁴	√	—	√	图像预处理及数字识别分类	[41]
	ITO/ZnO/Ag	白光	金属导电细丝	2/–2	—	√	√	√	人脸识别	[44]
	NiO/TiO₂/FTO	UV	界面效应	—	> 10	√	√	√	识别分类图像	[59]
触觉	Au/Nafion/ITO	压力	质子迁移	—	—	√	√	—	手写字母识别	[61]
	NiO/ZnO/ITO/PET	应变	界面效应	—	—	√	√	√	外部应变的时空信息处理	[62]
	Si/NbO_x/TiN	压力	晶体NbO₂通道	V_TH = 2.05 V V_H = 1.53 V	—	—	—	—	将压力模拟信号转换为动态振荡频率	[63]
	ITO/ZnO/NSTO	压力	界面效应	—	1×10⁴	√	√	—	识别和记忆手写字母和单词	[64]
	Al/TiO₂/Al	压力	氧空位导电细丝	—	14.2	√	—	√	压力实时感知、学习/推理、反馈可视化图像	[65]
	Pt/HfO₂/TiN	压力	氧空位导电细丝	0.9–1.1/–1	> 100	√	—	√	触觉记忆学习	[66]
	ZnO/PVA基忆阻器	压力	界面效应	V_TH = 3.25 V	1 × 10³	√	√	√	识别压力分布, 触觉可视化	[68]
嗅觉	Pd/W/WO₃/Pd	乙醇、甲烷、乙烯、一氧化碳	氧空位导电细丝	—	—	√	—	√	气体识别	[73]
嗅觉	Ti/rGO-CS/Au	H₂S	界面效应	—	—	√	√	—	气体识别	[75]

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