面向类脑计算的氧化物忆阻器

doi:10.15541/jim20230066

无机材料学报 ›› 2023, Vol. 38 ›› Issue (10): 1149-1162.DOI: 10.15541/jim20230066 CSTR: 32189.14.10.15541/jim20230066

所属专题：【信息功能】敏感陶瓷（202409）；【信息功能】神经形态材料与器件(202409)

面向类脑计算的氧化物忆阻器

诸葛霞¹(), 朱仁祥¹, 王建民¹, 王敬蕊¹, 诸葛飞²^,³^,⁴^,⁵()

1.宁波工程学院电子与信息工程学院, 宁波 315211
2.中国科学院宁波材料技术与工程研究所, 宁波 315201
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:2023-02-09 修回日期:2023-03-01 出版日期:2023-10-20 网络出版日期:2023-03-24
通讯作者: 诸葛飞, 研究员. E-mail: zhugefei@nimte.ac.cn
作者简介:诸葛霞(1979-), 女, 博士, 讲师. E-mail: zhugexia@nbut.edu.cn
基金资助:
国家自然科学基金(U20A20209);国家自然科学基金(61874125);中国科学院战略性先导专项(XDB32050204);环境友好能源材料国家重点实验室开放基金(20kfhg09);宁波市自然科学基金(2021J139);硅材料国家重点实验室开放基金(SKL2021-03)

Oxide Memristors for Brain-inspired Computing

ZHUGE Xia¹(), ZHU Renxiang¹, WANG Jianmin¹, WANG Jingrui¹, ZHUGE Fei²^,³^,⁴^,⁵()

1. School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315211, China
2. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, 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:2023-02-09 Revised:2023-03-01 Published:2023-10-20 Online:2023-03-24
Contact: ZHUGE Fei, professor. E-mail: zhugefei@nimte.ac.cn
About author:ZHUGE Xia (1979-), female, PhD, lecturer. E-mail: zhugexia@nbut.edu.cn
Supported by:
National Natural Science Foundation of China(U20A20209);National Natural Science Foundation of China(61874125);Strategic Priority Research Program of Chinese Academy of Sciences(XDB32050204);State Key Laboratory for Environment-Friendly Energy Materials(20kfhg09);Ningbo Natural Science Foundation of China(2021J139);State Key Laboratory of Silicon Materials(SKL2021-03)

摘要/Abstract

摘要：

类脑神经形态计算通过电子或光子器件集成来模拟人脑结构和功能。人工突触是类脑系统中数量最多的计算单元。忆阻器可模拟突触功能, 并具有优异的尺寸缩放性和低能耗, 是实现人工突触的理想元器件。利用欧姆定律和基尔霍夫定律, 忆阻器交叉阵列可执行并行的原位乘累加运算, 从而大幅提升类脑系统处理模拟信号的速度。氧化物制备容易, 和CMOS工艺兼容性强, 是使用最广泛的忆阻器材料。本文梳理了氧化物忆阻器的研究进展, 分别讨论了电控、光电混合调控和全光控忆阻器, 主要聚焦阻变机理、器件结构和性能。电控忆阻器工作一般会产生微结构变化和焦耳热, 将严重影响器件稳定性, 改进器件结构和材料成分可有效改善器件性能。利用光信号调控忆阻器电导, 不仅能降低能耗, 而且可避免产生微结构变化和焦耳热, 从而有望解决稳定性难题。此外, 光控忆阻器能直接感受光刺激, 单器件即可实现感/存/算功能, 可用于研发新型视觉传感器。因此, 全光控忆阻器的实现为忆阻器的研究和应用打开了一扇新窗口。

关键词: 氧化物忆阻器, 光电器件, 人工突触, 类脑神经形态计算, 综述

Abstract:

Brain-inspired neuromorphic computing refers to simulation of the structure and functionality of the human brain via the integration of electronic or photonic devices. Artificial synapses are the most abundant computation element in the brain-inspired system. Memristors are considered to be ideal devices for artificial synapse applications because of their high scalability and low power consumption. Based on Ohm’s law and Kirchhoff’s law, memristor crossbar arrays can perform parallel multiply-accumulate operations in situ, leading to analogue computing with greatly improved speed and energy efficiency. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with CMOS processes. This work reviews the research progress of oxide memristors for brain-inspired computing, mainly focusing on their resistance switching mechanisms, device structures and performances. These devices fall into three categories: electrical memristors, memristors controlled via both electrical and optical stimuli, and all-optically controlled memristors. The working mechanisms of electrical memristors are commonly related to microstructure change and Joule heat that are detrimental to device stability. The device performance can be improved by optimizing device structure and material composition. Tuning the device conductance with optical signals can avoid microstructure change and Joule heat as well as reducing energy consumption, thus making it possible to address the stability problem. In addition, optically controlled memristors can directly response to external light stimulus enabling integrated sensing-computing-memoring within single devices, which are expected to be used for developing next-generation vision sensors. Hence, the realization of all-optically controlled memristors opens a new window for research and applications of memristors.

Key words: oxide memristor, optoelectronic device, artificial synapse, brain-inspired neuromorphic computing, review

中图分类号:

O472

诸葛霞, 朱仁祥, 王建民, 王敬蕊, 诸葛飞. 面向类脑计算的氧化物忆阻器[J]. 无机材料学报, 2023, 38(10): 1149-1162.

ZHUGE Xia, ZHU Renxiang, WANG Jianmin, WANG Jingrui, ZHUGE Fei. Oxide Memristors for Brain-inspired Computing[J]. Journal of Inorganic Materials, 2023, 38(10): 1149-1162.

图/表 5

图1 基于TiO2-x忆阻器的单层感知器用于图像分类[74]

Fig. 1 Pattern classification using a single-layer perceptron based on TiO2-x memristor[74] (a) Mathematical abstraction of the perceptron; (b) 3×3 binary images; (c) Two sets of images used for classification; (d) Memristive crossbar circuit for the perceptron; (e) Current difference histograms for 50 input images at different training epochs

图2 Pt/TaOx/TiO2-x/Pt 忆阻器[75]

Fig. 2 Pt/TaOx/TiO2-x/Pt memristor[75] (a) Nonlinear current-voltage curve; (b) Schematic of the sneak path

图3 Cu/Ta2O5/Pt忆阻器[108]

Fig. 3 Cu/Ta2O5/Pt memristor[108] (a) Typical current-voltage curves; (b-e) Resistive switching mechanism for Set process; (f-i) Resistive switching mechanism for Reset process

图4 OD-IGZO/OR-IGZO全光控忆阻器[34]

Fig. 4 All-optically controlled memristor based on OD-IGZO/OR-IGZO[34] (a) Optical Set behavior upon irradiation with light of various wavelengths; (b) Photocurrent responses to irradiation with light of various wavelengths after blue light irradiation; (c) Synaptic potentiation process of spike-timing dependent plasticity;(d) Synaptic depression process of spike-timing dependent plasticity

图5 ZnO全光控忆阻器[35]

Fig. 5 ZnO-based all-optically controlled memristor[35] (a) Photocurrent responses to irradiation with light of various long wavelengths after short-wavelength light irradiation; (b) Nonvolatile logic computing. Colorful figures are available on website

参考文献 145

[1]	DRACHMAN D A. Do we have brain to spare. Neurology, 2005, 64(12): 2004. DOI URL
[2]	LI Z X, GENG X Y, WANG J, et al. Emerging artificial neuron devices for probabilistic computing. Frontiers in Neuroscience, 2021, 15: 717947.
[3]	MEAD C. Neuromorphic electronic systems. Proceedings of the IEEE, 1990, 78(10): 1629. DOI URL
[4]	MEROLLA P A, ARTHUR J V, ALVAREZ-ICAZA R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345(6197): 668. DOI URL
[5]	DIORIO C, HASLER P, MINCH B A, et al. A single-transistor silicon synapse. IEEE Transactions Electron Devices, 1996, 43(11): 1972. DOI URL
[6]	FULLER E J, KEENE S T, MELIANAS A, et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science, 2019, 364(6440): 570. DOI PMID
[7]	WANG Z, JOSHI S, SAVEL’EV S, et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nature Electronics, 2018, 1(2): 137. DOI
[8]	WANG J, ZHUGE F. Memristive synapses for brain-inspired computing. Advanced Materials Technologies, 2019, 4(3): 1800544. DOI URL
[9]	WANG Z, ZENG T, REN Y, et al. Toward a generalized Bienenstock- Cooper-Munro rule for spatiotemporal learning via triplet-STDP in memristive devices. Nature Communications, 2020, 11: 1510.
[10]	SENGUPTA A, AZIM Z A, FONG X, et al. Spin-orbit torque induced spike-timing dependent plasticity. Applied Physics Letters, 2015, 106(9): 093704. DOI URL
[11]	CHUA L. Memristor-the missing circuit element. IEEE Transactions on Circuit Theory, 1971, 18(5): 507. DOI URL
[12]	CHUA L. Resistance switching memories are memristors. Applied Physics A-Materials Science＆Processing, 2011, 102: 765.
[13]	STRUKOV D B, SNIDER G S, STEWART D R, et al. The missing memristor found. Nature, 2008, 453: 80.
[14]	PI S, LI C, JIANG H, et al. Memristor crossbar arrays with 6-nm half-pitch and 2-nm critical dimension. Nature Nanotechnology, 2019, 14: 35.
[15]	WANG T Y, MENG J L, RAO M Y, et al. Three-dimensional nanoscale flexible memristor networks with ultralow power for information transmission and processing application. Nano Letters, 2020, 20(6): 4111. DOI URL
[16]	WILLIAMS R S. What’s next? Computing in Science＆ Engineering, 2017, 19(2): 7.
[17]	YANG C, SUN B, ZHOU G, et al. Photoelectric memristor-based machine vision for artificial intelligence applications. ACS Materials Letters, 2023, 5(2): 504. DOI URL
[18]	WU X, DANG B, WANG H, et al. Spike-enabled audio learning in multilevel synaptic memristor array-based spiking neural network. Advanced Intelligent Systems, 2021, 4(3): 2100151. DOI URL
[19]	WANG C, YANG Z, WANG S, et al. A braitenberg vehicle based on memristive neuromorphic circuits. Advanced Intelligent Systems, 2020, 2(1): 1900103. DOI URL
[20]	WANG Y, GONG Y, HUANG S, et al. Memristor-based biomimetic compound eye for real-time collision detection. Nature Communications, 2021, 12: 5979.
[21]	PARK S-O, JEONG H, PARK J, et al. Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing. Nature Communications, 2022, 13: 2888.
[22]	LIU Z, TANG J, GAO B, et al. Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces. Nature Communications, 2020, 11: 4234.
[23]	HAMDIOUI S, XIE L, NGUYEN H A D, et al.Memristor based computation-in-memory architecture for data-intensive applications. Design, Automation and Test in Europe Conference and Exhibition, Grenoble, 2015: 1718.
[24]	PREZIOSO M, MERRIKH-BAYAT F, HOSKINS B D, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 2015, 521: 61.
[25]	SHERIDAN P M, CAI F X, DU C, et al. Sparse coding with memristor networks. Nature Nanotechnology, 2017, 12: 784.
[26]	HU M, GRAVES C E, LI C, et al. Memristor-based analog computation and neural network classification with a dot product engine. Advanced Materials, 2018, 30(9): 1705914. DOI URL
[27]	YAO P, WU H Q, GAO B, et al. Face classification using electronic synapses. Nature Communications, 2017, 8: 15199.
[28]	YAO P, WU H Q, GAO B, et al. Fully hardware-implemented memristor convolution neural network. Nature, 2020, 577: 641.
[29]	ZHUGE F, LI K, FU B, et al. Mechanism for resistive switching in chalcogenide-based electrochemical metallization memory cells. AIP Advances, 2015, 5(5): 057125. DOI URL
[30]	ZHANG S R, ZHOU L, MAO J Y, et al. Artificial synapse emulated by charge trapping-based resistive switching device. Advanced Materials Technologies, 2019, 4(2): 1800342. DOI URL
[31]	ZHUGE F, DAI W, HE C L, et al. Nonvolatile resistive switching memory based on amorphous carbon. Applied Physics Letters, 2010, 96(16): 163505. DOI URL
[32]	ZHUGE F, HU B, HE C, et al. Mechanism of nonvolatile resistive switching in graphene oxide thin films. Carbon, 2011, 49: 3796.
[33]	ZHUGE F, LI J, CHEN H, et al. Single-crystalline metal filament- based resistive switching in a nitrogen-doped carbon film containing conical nanopores. Applied Physics Letters, 2015, 106(8): 083104. DOI URL
[34]	HU L, YANG J, WANG J, et al. All-optically controlled memristor for optoelectronic neuromorphic computing. Advanced Functional Materials, 2021, 31(4): 2005582. DOI URL
[35]	YANG J, HU L, SHEN L, et al. Optically driven intelligent computing with ZnO memristor. Fundamental Research, DOI: 10.1016/j.fmre.2022.06.019.
[36]	STRACGAN J P, PICKETT M D, YANG J J, et al. Direct identification of the conducting channels in a functioning memristive device. Advanced Materials, 2010, 22(32): 3573. DOI URL
[37]	KWON D, KIM K, JANG J H, et al. Atomic structure of conducting nanofilaments in TiO₂ resistive switching memory. Nature Nanotechnology, 2010, 5: 148.
[38]	NAGASHIMA K, YANAGIDA T, OKA K, et al. Unipolar resistive switching characteristics of room temperature grown SnO₂ thin films. Applied Physics Letters, 2009, 94(24): 242902. DOI URL
[39]	CAO X, LI X, GAO X, et al. Forming free colossal resistive switching effect in rare-earth-oxide Gd₂O₃ films for memristor applications. Journal of Applied Physics, 2009, 106(7): 073723. DOI URL
[40]	SUN X, SUN B, LOU L, et al. Resistive switching in CeO_x films for nonvolatile memory application. IEEE Electron Device Letters, 2009, 30(4): 334. DOI URL
[41]	HUANG H H, SHIH W C, LAI C H. Nonpolar resistive switching in the Pt/MgO/Pt nonvolatile memory device. Applied Physics Letters, 2010, 96(19): 193505. DOI URL
[42]	ZHANG H, GAO B, SUN B, et al. Ionic doping effect in ZrO₂ resistive switching memory. Applied Physics Letters, 2010, 96(12): 123502. DOI URL
[43]	CHIEN W C, CHEN Y C, LAI E K, et al. Unipolar switching behaviors of RTO WO_x RAM. IEEE Electron Device Letters, 2010, 31(2): 126. DOI URL
[44]	YANG M K, PARK J W, KO T K, et al. Resistive switching characteristics of TiN/MnO₂/Pt memory devices. Physics Status Solidi-Rapid Research Letters, 2010, 4(8/9): 233. DOI URL
[45]	GAO X, XIA Y, JI J, et al. Effect of top electrode materials on bipolar resistive switching behavior of gallium oxide films. Applied Physics Letters, 2010, 97(19): 193501. DOI URL
[46]	TULINA N A, BORISENKO I Y, IONOV A M, et al. Bipolar resistive switching in heterostructures: bismuth oxide/normal metal. Solid State Communications, 2010, 150(43/44): 2089. DOI URL
[47]	CHEN S C, CHANG T C, CHEN S Y, et al. Bipolar resistive switching of chromium oxide for resistive random access memory. Solid-State Electronics, 2011, 62(1): 40. DOI URL
[48]	YAO J, ZHONG L, NATELSON D, et al. Intrinsic resistive switching and memory effects in silicon oxide. Applied Physics A-Materials Science＆Processing, 2011, 102: 835.
[49]	HSU C H, LIN J S, HE Y D, et al. Optical, electrical properties and reproducible resistance switching of GeO₂ thin films by Sol-Gel process. Thin Solid Films, 2011, 519(15): 5033. DOI URL
[50]	ARITA M, KAJI H, FUJI T, et al. Resistive switching properties of molybdenum oxide films. Thin Solid Films, 2012, 520(14): 4762. DOI URL
[51]	AHN Y, LEE J H, KIM G H, et al. Concurrent presence of unipolar and bipolar resistive switching phenomena in pnictogen oxide Sb₂O₅ films. Journal of Applied Physics, 2012, 112(11): 114105. DOI URL
[52]	PI C, REN Y, LIU Z Q, et al. Unipolar memristive switching in yttrium oxide and RESET current reduction using a yttrium interlayer. Electrochemical and Solid-State Letters, 2012, 15(3): G5. DOI URL
[53]	LIN Y S, ZENG F, TANG S G, et al. Resistive switching mechanisms relating to oxygen vacancies migration in both interfaces in Ti/HfO_x/Pt memory devices. Journal of Applied Physics, 2013, 113(6): 064510. DOI URL
[54]	CHOI D, KIM C S. Coexistence of unipolar and bipolar resistive switching in Pt/NiO/Pt. Applied Physics Letters, 2014, 104(19): 193507. DOI URL
[55]	CHEN X, ZHANG H, RUAN K, et al. Annealing effect on the bipolar resistive switching behaviors of BiFeO₃ thin films on LaNiO₃-buffered Si substrates. Journal of Alloys and Compounds, 2012, 529: 108.
[56]	WASER R, DITTMANN R, STAIKOV G, et al. Redox-based resistive switching memories-nanoionic mechanisms, prospects, and challenges. Advanced Materials, 2009, 21(25/26): 2632. DOI URL
[57]	YANG J J, STRUKOV D B, STEWART D R. Memristive devices for computing. Nature Nanotechnology, 2013, 8: 13.
[58]	YANG J J, STRACHAN J P, XIA Q F, et al. Diffusion of adhesion layer metals controls nanoscale memristive switching. Advanced Materials, 2010, 22(36): 4034. DOI URL
[59]	YANG J J, PICKET M D, LI X, et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nature Nanotechnology, 2008, 3: 429.
[60]	YANG J J, MIAO F, PICKETT M D, et al. The mechanism of electroforming of metal oxide memristive switches. Nanotechnology, 2009, 20(21): 215201. DOI URL
[61]	YANG J J, STRACHAN J P, MIAO F, et al. Metal/TiO₂ interfaces for memristive switches. Applied Physics A-Materials Science＆Processing, 2011, 102: 785.
[62]	PICKETT M D, BORGHETTI J, YANG J J, et al. Coexistence of memristance and negative differential resistance in a nanoscale metal-oxide-metal system. Advanced Materials, 2011, 23(15): 1730. DOI URL
[63]	MIAO F, YANG J J, BORGHETTI J, et al. Observation of two resistance switching modes in TiO₂ memristive devices electroformed at low current. Nanotechnology, 2011, 22(25): 254007. DOI URL
[64]	YOON K J, LEE M H, KIM G H, et al. Memristive tri-stable resistive switching at ruptured conducting filaments of a Pt/TiO₂/Pt cell. Nanotechnology, 2012, 23(18): 185202. DOI URL
[65]	JEONG H Y, LEE J Y, CHOI S Y. Interface-engineered amorphous TiO₂-based resistive memory devices. Advanced Functional Materials, 2010, 20(22): 3912. DOI URL
[66]	YANG J J, ZHANG M X, STRACHAN J P, et al. High switching endurance in TaO_x memristive devices. Applied Physics Letters, 2010, 97(23): 232102. DOI URL
[67]	QI J, OLMEDO M, REN J, et al. Resistive switching in single epitaxial ZnO nanoislands. ACS Nano, 2012, 6(2): 1051. DOI PMID
[68]	WANG W, PEDRETTI G, MILO V, et al. Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses. Science Advances, 2018, 4(9): eaat4752. DOI URL
[69]	WANG W, PREDRETTI G, MILO V, et al. Computing of temporal information in spiking neural networks with ReRAM synapses. Faraday Discussions, 2019, 213: 453.
[70]	CHANDRASEKARAN S, SIMANJUNTAK F M, SAMINATHAN R, et al. Improving linearity by introducing Al in HfO₂ as memristor synapse device. Nanotechnology, 2019, 30(44): 445205. DOI URL
[71]	SUN X, ZHANG T, CHENG C, et al. A memristor-based in-memory computing network for Hamming code error correction. IEEE Electron Device Letters, 2019, 40(7): 1080. DOI URL
[72]	PARK J, PARK E, KIM S, et al. Nitrogen-induced enhancement of synaptic weight reliability in titanium oxide-based resistive artificial synapse and demonstration of the reliability effect on the neuromorphic system. ACS Applied Materials＆Interfaces, 2019, 11(35): 32178.
[73]	WU P Y, ZHENG H X, SHIH C C, et al. Improvement of resistive switching characteristics in zinc oxide-based resistive random access memory by ammoniation annealing. IEEE Electron Device Letters, 2020, 41(3): 357. DOI URL
[74]	ALIBART F, ZAMANIDOOST E, STRUKOV D B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nature Communications, 2013, 4: 2072. DOI
[75]	YANG J J, ZHANG M-X, PICKETT M D, et al. Engineering nonlinearity into memristors for passive crossbar applications. Applied Physics Letters, 2012, 100(11): 113501. DOI URL
[76]	KIM S, ABBAS Y, JEON Y R, et al. Engineering synaptic characteristics of TaO_x/HfO₂ bi-layered resistive switching device. Nanotechnology, 2018, 29(41): 415204. DOI URL
[77]	LIU L, XIONG W, LIU Y, et al. Designing high-performance storage in HfO₂/BiFeO₃ memristor for artificial synapse applications. Advanced Electronic Materials, 2020, 109(22): 1901012.
[78]	LEE M-J, LEE C B, LEE D, et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta₂O₅_-x/TaO_2-_x bilayer structures. Nature Materials, 2011, 10: 625.
[79]	LIU J, YANG H, JI Y, et al. An electronic synaptic device based on HfO₂/TiO_x bilayer structure memristor with self-compliance and deep-Reset characteristics. Nanotechnology, 2018, 29(41): 415205. DOI URL
[80]	YIN J, ZENG F, WAN Q, et al. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Advanced Functional Materials, 2018, 28(19): 1706927. DOI URL
[81]	WANG R, SHI T, ZHANG X, et al. Bipolar analog memristors as artificial synapses for neuromorphic computing. Materials, 2018, 11(11): 2102. DOI URL
[82]	HANSEN M, ZAHARI F, KOHLSTEDT H, et al. Unsupervised Hebbian learning experimentally realized with analogue memristive crossbar arrays. Scientific Reports, 2018, 8: 8914.
[83]	DANG B, WU Q, SONG F, et al. A bio-inspired physically transient/biodegradable synapse for security neuromorphic computing based on memristors. Nanoscale, 2018, 10(43): 20089. DOI PMID
[84]	BANG S, KIM M H, KIM T H, et al. Gradual switching and self-rectifying characteristics of Cu/α-IGZO/p⁺-Si RRAM for synaptic device application. Solid-State Electronics, 2018, 150: 60.
[85]	KIM H J, KIM M, BEOM K, et al. A Pt/ITO/CeO₂/Pt memristor with an analog, linear, symmetric, and long-term stable synaptic weight modulation. APL Materials, 2019, 7(7): 071113. DOI URL
[86]	ZHOU Y, WU H Q, GAO B, et al. Associative memory for image recovery with a high-performance memristor array. Advanced Functional Materials, 2019, 29(30): 1900155. DOI URL
[87]	SOKOLOV A S, JEON Y R, KIM S, et al. Bio-realistic synaptic characteristics in the cone-shaped ZnO memristive device. NPG Asia Materials, 2019, 11: 5.
[88]	XU H, ZHAI X, WANG Z, et al. An epitaxial synaptic device made by a band-offset BaTiO₃/Sr₂IrO₄ bilayer with high endurance and long retention. Applied Physics Letters, 2019, 114(10): 102904. DOI URL
[89]	SOKOLOV A S, JEON Y R, KU B, et al. Ar ion plasma surface modification on the heterostructured TaO_x/InGaZnO thin films for flexible memristor synapse. Journal of Alloys and Compounds, 2020, 822: 153625.
[90]	MAHATA C, LEE C, AN Y, et al. Resistive switching and synaptic behaviors of an HfO₂/Al₂O₃ stack on ITO for neuromorphic systems. Journal of Alloys and Compounds, 2020, 826: 154434.
[91]	CHEN J Y, WU M C, TING Y H, et al. Applications of p-n homojunction ZnO nanowires to one-diode one-memristor RRAM arrays. Scripta Materialia, 2020, 187: 439.
[92]	HUANG X D, LI Y, LI H Y, et al. Forming-free, fast, uniform, and high endurance resistive switching from cryogenic to high temperatures in W/AlO_x/Al₂O₃/Pt bilayer memristor. IEEE Electron Device Letters, 2020, 41(4): 549. DOI URL
[93]	YIN X, WANG Y, CHANG T H, et al. Memristive behavior enabled by amorphous-crystalline 2D oxide heterostructure. Advanced Materials, 2020, 32(22): 2000801. DOI URL
[94]	ZHANG L, XU Z, HAN J, et al. Resistive switching performance improvement of InGaZnO-based memory device by nitrogen plasma treatment. Journal of Materials Science＆Technology, 2020, 49: 1.
[95]	BOUSOULAS P, MICHELAKAKI I, SKOTADIS E, et al. Low power forming free TiO_2-_x/Hf0_2-_x/TiO_2-_x-trilayer RRAM devices exhibiting synaptic property characteristics. IEEE Transactions on Electron Devices, 2017, 64(8): 3151. DOI URL
[96]	YU S, GAO B, FANG Z, et al. A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation. Advanced Materials, 2013, 25(12): 1774. DOI URL
[97]	BESSONOV A A, KIRIKOVA M N, PETUKHOV D I, et al. Layered memristive and memcapacitive switches for printable electronics. Nature Materials, 2015, 14(2): 199. DOI PMID
[98]	WANG C, HE W, TONG Y, et al. Memristive devices with highly repeatable analog states boosted by graphene quantum dots. Small, 2017, 13(20): 1603435. DOI URL
[99]	TAO Y, WANG Z, XU H, et al. Moisture-powered memristor with interfacial oxygen migration for power-free reading of multiple memory states. Nano Energy, 2020, 71: 104628.
[100]	SCHINDLER C, THERMADAM S C P, WASER R, et al. Bipolar and unipolar resistive switching in Cu-doped SiO₂. IEEE Transactions on Electron Devices, 2007, 54(10): 2762. DOI URL
[101]	HAEMORI M, NAGATA T, CHIKYOW T. Impact of Cu electrode on switching behavior in a Cu/HfO₂/Pt structure and resultant Cu ion diffusion. Applied Physics Express, 2009, 2(6): 061401. DOI URL
[102]	LI Y, LONG S, ZHANG M, et al. Resistive switching properties of Au/ZrO₂/Ag structure for low-voltage nonvolatile memory applications. IEEE Electron Device Letters, 2010, 31(2): 117. DOI URL
[103]	YAN X B, LI K, YIN J, et al. The resistive switching mechanism of Ag/SrTiO₃/Pt memory cells. Electrochemical and Solid-State Letters, 2010, 13(3): H87. DOI URL
[104]	LI Y, LONG S, LIU Q, et al. Nonvolatile multilevel memory effect in Cu/WO₃/Pt device structures. Physics Status Solidi-Rapid Research Letters, 2010, 4(5/6): 124. DOI URL
[105]	PENG S, ZHUGE F, CHEN X, et al. Mechanism for resistive switching in an oxide-based electrochemical metallization memory. Applied Physics Letters, 2012, 100(7): 072101. DOI URL
[106]	VALOV I, LINN E, TAPPERTZHOFEN S, et al. Nanobatteries in redox-based resistive switches require extension of memristor theory. Nature Communications, 2013, 4: 1771.
[107]	TSUNODA K, FUKUZUMI Y, JAMESON J, et al. Bipolar resistive switching in polycrystalline TiO₂ films. Applied Physics Letters, 2007, 90(11): 113501. DOI URL
[108]	TSURUOKA T, TERABE K, HASEGAWA T, et al. Forming and switching mechanisms of a cation-migration-based oxide resistive memory. Nanotechnology, 2010, 21(42): 425205. DOI URL
[109]	WEDIG A, LUEBBEN M, CHO D Y, et al. Nanoscale cation motion in TaO_x, HfO_x and TiO_x memristive systems. Nature Nanotechnology, 2016, 11: 67.
[110]	JIANG H, HAN L, LIN P, et al. Sub-10 nm Ta channel responsible for superior performance of a HfO₂ memristor. Scientific Reports, 2016, 6: 28525.
[111]	CHEN W, FANG R, BALABAN M B, et al. A CMOS-compatible electronic synapse device based on Cu/SiO₂/W programmable metallization cells. Nanotechnology, 2016, 27(25): 255202. DOI URL
[112]	WANG Z, JOSHI S, SAVEL’EV S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nature Materials, 2017, 16(1): 101.
[113]	LUBBEN M, CUPPERS F, MOHR J, et al. Design of defect- chemical properties and device performance in memristive systems. Science Advances, 2020, 6(19): eaaz9079. DOI URL
[114]	GUO X, WANG Q, LV X, et al. SiO₂/Ta₂O₅ heterojunction ECM memristors: physical nature of their low voltage operation with high stability and uniformity. Nanoscale, 2020, 12(7): 4320. DOI URL
[115]	ALI A, ABBAS Y, ABBAS H, et al. Dependence of InGaZnO and SnO₂ thin film stacking sequence for the resistive switching characteristics of conductive bridge memory devices. Applied Surface Science, 2020, 525: 146390.
[116]	CHANG C F, CHEN J Y, HUANG G M, et al. Revealing conducting filament evolution in low power and high reliability Fe₃O₄/Ta₂O₅ bilayer RRAM. Nano Energy, 2018, 53: 871.
[117]	HU Q, LI R, ZHANG X, et al. Lithium ion trapping mechanism of SiO₂ in LiCoO₂ based memristors. Scientific Reports, 2019, 9: 5081.
[118]	IOANNOU P S, KYRIAKIDES E, SCHNEEGANS O, et al. Evidence of biorealistic synaptic behavior in diffusive Li-based two- terminal resistive switching devices. Scientific Reports, 2020, 10: 8711.
[119]	YAN X, ZHANG L, CHEN H, et al. Graphene oxide quantum dots based memristors with progressive conduction tuning for artificial synaptic learning. Advanced Functional Materials, 2018, 28(40): 1803728. DOI URL
[120]	LIM S, KWAK M, HWANG H. Improved synaptic behavior of CBRAM using internal voltage divider for neuromorphic systems. IEEE Transactions Electron Devices, 2018, 65(9): 3976. DOI URL
[121]	LIM S, KWAK M, HWANG H. One transistor-two resistive RAM device for realizing bidirectional and analog neuromorphic synapse devices. Nanotechnology, 2019, 30(45): 455201. DOI URL
[122]	LIM S, SUNG C, KIM H, et al. Improved synapse device with MLC and conductance linearity using quantized conduction for neuromorphic systems. IEEE Electron Device Letters, 2018, 39(2): 312. DOI URL
[123]	YAN X, PEI Y, CHEN H, et al. Self-assembled networked PbS distribution quantum dots for resistive switching and artificial synapse performance boost of memristors. Advanced Materials, 2019, 31(7): 1805284. DOI URL
[124]	LU Y F, LI Y, LI H Y, et al. Low-power artificial neurons based on Ag/TiN/HfAlO_x/Pt threshold switching memristor for neuromorphic computing. IEEE Electron Device Letters, 2020, 41(8): 1245. DOI URL
[125]	KUMAR M, ABBAS S, LEE J-H, et al. Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm. Nanoscale, 2019, 11(33): 15596. DOI PMID
[126]	YAN X, QIN C, LU C, et al. Robust Ag/ZrO₂/WS₂/Pt memristor for neuromorphic computing. ACS Applied Materials＆Interfaces, 2019, 11(51): 48029.
[127]	CHOI Y, LEE C, KIM M, et al. Structural engineering of Li based electronic synapse for high reliability. IEEE Electron Device Letters, 2019, 40(12): 1992. DOI URL
[128]	PAN R, LI J, ZHUGE F, et al. Synaptic devices based on purely electronic memristors. Applied Physics Letters, 2016, 108(1): 013504. DOI URL
[129]	WANG J, PAN R, CAO H, et al. Anomalous rectification in a purely electronic memristor. Applied Physics Letters, 2016, 109(14): 143505. DOI URL
[130]	KUZMICHEV D S, CHERNIKOVA A G, KOZODAEV M G, et al. Resistance switching peculiarities in nonfilamentary self-rectified TiN/Ta₂O₅/Ta and TiN/HfO₂/Ta₂O₅/Ta stacks. Physics Status Solidi-Rapid Research Letters, 2020, 217(18): 1900952.
[131]	XU Z, LI F, WU C, et al. Ultrathin electronic synapse having high temporal/spatial uniformity and an Al₂O₃/graphene quantum dots/Al₂O₃ sandwich structure for neuromorphic computing. NPG Asia Materials, 2019, 11: 18.
[132]	MA F, XU Z, LIU Y, et al. Highly-reliable electronic synapse based on Au@Al₂O₃ core-shell nanoparticles for neuromorphic applications. IEEE Electron Device Letters, 2019, 40(10): 1610. DOI URL
[133]	KWON D E, KIM J, KWON Y J, et al. Area-type electronic bipolar resistive switching of Pt/Al₂O₃/Si₃N_3.0/Ti with forming-free, self-rectification, and nonlinear characteristics. Physics Status Solidi-Rapid Research Letters, 2020, 14(8): 2000209. DOI URL
[134]	PARK J, LEE S, YONG K. Photo-stimulated resistive switching of ZnO nanorods. Nanotechnology, 2012, 23(38): 385707. DOI URL
[135]	ZHOU Y, YEW K S, ANG D S, et al. White-light-induced disruption of nanoscale conducting filament in hafnia. Applied Physics Letters, 2015, 107(7): 072107. DOI URL
[136]	ZHOU F, ZHOU Z, CHEN J, et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nature Nanotechnology, 2019, 14: 776.
[137]	BERA A, PENG H, LOUREMBAM J, et al. A versatile light-switchable nanorod memory: wurtzite ZnO on perovskite SrTiO₃. Advanced Functional Materials, 2013, 23(39): 4977. DOI URL
[138]	HU D-C, YANG R, JIANG L, et al. Memristive synapses with photoelectric plasticity realized in ZnO_1-_x/AlO_y heterojunction. ACS Applied Materials＆Interfaces, 2018, 10(7): 6463.
[139]	ZHUGE X, WANG J, ZHUGE F. Photonic synapses for ultrahigh- speed neuromorphic computing. Physics Status Solidi-Rapid Research Letters, 2019, 13(9): 1900082. DOI URL
[140]	ZHU J, ZHANG T, YANG Y, et al. A comprehensive review on emerging artificial neuromorphic devices. Applied Physics Reviews, 2020, 7: 011312.
[141]	SHAN X, ZHAO C, WANG X, et al. Plasmonic optoelectronic memristor enabling fully light-modulated synaptic plasticity for neuromorphic vision. Advanced Science, 2022, 9(6): 2104632. DOI URL
[142]	HICKMOTT T W. Low-frequency negative resistance in thin anodic oxide films. Journal of Applied Physics, 1962, 33(9): 2669. DOI URL
[143]	CHOI B J, TORREZAN A C, NORRIS K J, et al. Electrical performance and scalability of Pt dispersed SiO₂ nanometallic resistance switch. Nano Letters, 2013, 13(7): 3213. DOI URL
[144]	KUZUM D, YU S, WONG H S P. Synaptic electronics: materials, devices and applications. Nanotechnology, 2013, 24(38): 382001. DOI URL
[145]	沈柳枫, 胡令祥, 康逢文, 等. 光电神经形态器件及其应用. 物理学报, 2022, 71(14): 148508.

面向类脑计算的氧化物忆阻器

Oxide Memristors for Brain-inspired Computing

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