基于忆阻器的感存算一体技术综述

张章; 李超; 韩婷婷; 许傲; 程心; 刘钢; 解光军

doi:10.11999/JEIT201102

基于忆阻器的感存算一体技术综述

doi: 10.11999/JEIT201102

张章¹,
李超¹,
韩婷婷¹,
许傲¹,
程心^1, ,,
刘钢²,
解光军¹

1.
合肥工业大学电子科学与应用物理学院合肥 230601
2.
上海交通大学电子信息与电气学院上海 200240

基金项目: 国家自然科学基金(U19A2053, 61674049)，中央高校基本科研业务费(JZ2020YYPY0089)，中国科学院红外成像材料与器件重点实室开放课题(IMDKFJJ-19-04)

详细信息

作者简介:
张章：男，1982年生，教授，研究方向为集成电路设计及基于混合器件的AI神经形态芯片设计

李超：男，1998年生，硕士生，研究方向为基于混合器件的AI神经形态芯片设计

韩婷婷：女，1998年生，硕士生，研究方向为基于混合器件的AI神经形态芯片设计

许傲：男，1998年生，硕士生，研究方向为基于混合器件的AI神经形态芯片设计

程心：女，1985年生，副教授，研究方向为集成电路设计

刘钢：男，1982年生，教授，研究方向为基于新型半导体材料的非冯忆阻逻辑器件与神经形态器件

解光军：男，1970年生，教授，研究方向为集成电路及新型器件电路设计

通讯作者:
程心　xcheng@hfut.edu.cn

中图分类号: TN601
计量
- 文章访问数: 4519
- HTML全文浏览量: 1957
- PDF下载量: 869
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-31
- 修回日期: 2021-03-18
- 网络出版日期: 2021-04-02
- 刊出日期: 2021-06-18

Review of the Fused Technology of Sensing, Storage and Computing Based on Memristor

1.
School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei 230601, China
2.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Funds: The National Natural Science Foundation of China (U19A2053, 61674049), The Fundamental Research Funds for Central Universities (JZ2020YYPY0089), Key Laboratory of CAS (IMDKFJJ-19-04)

摘要

摘要: 忆阻器的低功耗、高响应、纳米级、非易失性等特性，在实现非冯·诺依曼计算架构中展现出巨大潜力。基于忆阻器的高密度横梁阵列可实现数据存储及并行计算一体的逻辑电路和类脑计算电路。此外，纳米传感器与忆阻器进一步集成，采集的信号直接送往忆阻器阵列进行运算和存储，感知、存储与计算一体化的芯片技术成为新的研究热点。该文对基于忆阻器的存算一体技术、感存算一体技术的研究现状进行综述，并给出研究前景展望。
- 忆阻器 /
- 感存算一体 /
- 非冯·诺依曼计算架构
Abstract: Because of its low power consumption, high response, nanometer level, non-volatility and other characteristics, the memristor shows great development potential in the realization of non-von Neumann computing architecture. The high-density cross-array structure based on memristors can build logic circuits and brain-like computing circuits integrating data storage and parallel computing. In addition, the nanosensor and the memristor are further integrated, and the collected signals are calculated and stored in the memristor array. The chip technology integrating sensing, storage and computing become a new research focus. The research on the memristor-based storage-calculation integrated technology and sense-storage-calculated integrated technology are reviewed in this summary paper, and outding prospect of the research prospects are given.
- Memristor /
- Integration of sensing, storage and computing /
- Non-von Neumann

HTML全文

图 1 基于忆阻器的逻辑单元

下载: 全尺寸图片幻灯片

图 2 基于忆阻器的类脑电路

下载: 全尺寸图片幻灯片

图 3 触觉感存算一体技术

下载: 全尺寸图片幻灯片

图 4 视觉感存算一体技术

下载: 全尺寸图片幻灯片

图 5 嗅觉感存算一体技术

下载: 全尺寸图片幻灯片

图 6 听觉感存算一体技术

下载: 全尺寸图片幻灯片

图 7 感存算片上集成

下载: 全尺寸图片幻灯片

参考文献(97)

[1]	CHUA L. Memristor-the missing circuit element[J]. IEEE Transactions on Circuit Theory, 1971, 18(5): 507–519. doi: 10.1109/TCT.1971.1083337
[2]	LI Can, GRAVES C E, SHENG Xia, et al. Analog content-addressable memories with memristors[J]. Nature Communications, 2020, 11(1): 1638. doi: 0.1038/s41467-020-15254-4
[3]	CAO Qiang, LÜ Weiming, WANG Renshaw, et al. Nonvolatile multistates memories for high-density data storage[J]. ACS Applied Materials & Interfaces, 2020, 12(38): 42449–42471. doi: 10.1021/acsami.0c10184
[4]	ALI K A, RIZK M, BAGHDADI A, et al. Crossbar memory architecture performing memristor overwrite logic[C]. 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Genoa, Italy, 2019: 723–726. doi: 10.1109/ICECS46596.2019.8964910.
[5]	LUO Li, DONG Zhekang, DUAN Shukai, et al. Memristor-based stateful logic gates for multi-functional logic circuit[J]. IET Circuits, Devices & Systems, 2020, 14(6): 811–818. doi: 10.1049/iet-cds.2019.0422
[6]	ASCOLI A, SLESAZECK S, MÄHNE H, et al. Nonlinear dynamics of a locally-active memristor[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2015, 62(4): 1165–1174. doi: 10.1109/TCSI.2015.2413152
[7]	CORINTO F and FORTI M. Nonlinear dynamics of memristor oscillators via the flux-charge analysis method[C]. 2017 IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, USA, 2017. doi: 10.1109/ISCAS.2017.8050989.
[8]	WU Renping and WANG Chunhua. A new simple chaotic circuit based on memristor[J]. International Journal of Bifurcation and Chaos, 2016, 26(9): 1650145. doi: 10.1142/S0218127416501455
[9]	WANG Chunhua, XIA Hu, and ZHOU Ling. A memristive hyperchaotic multiscroll jerk system with controllable scroll numbers[J]. International Journal of Bifurcation and Chaos, 2017, 27(6): 1750091. doi: 10.1142/s0218127417500912
[10]	ZHU Minghao, WANG Chunhua, DENG Quanli, et al. Locally active memristor with three coexisting pinched hysteresis loops and its emulator circuit[J]. International Journal of Bifurcation and Chaos, 2020, 30(13): 2050184. doi: 10.1142/S0218127420501849
[11]	ZHOU Chao, WANG Chunhua, SUN Yichuang, et al. Weighted sum synchronization of memristive coupled neural networks[J]. Neurocomputing, 2020, 403: 211–233. doi: 10.1016/j.neucom.2020.04.087
[12]	YAO Wei, WANG Chunhua, SUN Yichuang, et al. Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations[J]. Applied Mathematics and Computation, 2020, 386: 125483. doi: 10.1016/j.amc.2020.125483
[13]	LIN Hairong, WANG Chunhua, HONG Qinghui, et al. A multi-stable memristor and its application in a neural network[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, 67(12): 3472–3476. doi: 10.1109/TCSII.2020.3000492
[14]	LIN Hairong, WANG Chunhua, SUN Yichuang, et al. Firing multistability in a locally active memristive neuron model[J]. Nonlinear Dynamics, 2020, 100(4): 3667–3683. doi: 10.1007/s11071-020-05687-3
[15]	LIN Hairong, WANG Chunhua, and TAN Yumei. Hidden extreme multistability with hyperchaos and transient chaos in a Hopfield neural network affected by electromagnetic radiation[J]. Nonlinear Dynamics, 2020, 99(4): 2369–2386. doi: 10.1007/s11071-019-05408-5
[16]	IELMINI D and WONG H S P. In-memory computing with resistive switching devices[J]. Nature Electronics, 2018, 1(6): 333–343. doi: 10.1038/s41928-018-0092-2
[17]	CHI Ping, LI Shuangchen, XU Cong, et al. PRIME: A novel processing-in-memory architecture for neural network computation in ReRAM-based main memory[C]. 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), Seoul, Korea, 2016: 27–39. doi: 10.1109/ISCA.2016.13.
[18]	BORGHETTI J, SNIDER G S, KUEKES P J, et al. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication[J]. Nature, 2010, 464(7290): 873–876. doi: 10.1038/nature08940
[19]	KVATINSKY S, BELOUSOV D, LIMAN S, et al. MAGIC—memristor-aided logic[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2014, 61(11): 895–899. doi: 10.1109/TCSII.2014.2357292
[20]	ALAMGIR Z, BECKMANN K, CADY N, et al. Flow-based computing on nanoscale crossbars: Design and implementation of full adders[C]. 2016 IEEE International Symposium on Circuits and Systems, Montreal, Canada, 2016: 1870–1873.
[21]	TALATI N, GUPTA S, MANE P, et al. Logic design within memristive memories using memristor-aided loGIC (MAGIC)[J]. IEEE Transactions on Nanotechnology, 2016, 15(4): 635–650. doi: 10.1109/TNANO.2016.2570248
[22]	LI H, GAO B, CHEN Z, et al. A learnable parallel processing architecture towards unity of memory and computing[J]. Scientific Reports, 2015, 5: 13330. doi: 10.1038/srep13330
[23]	CHENG Long, ZHANG Meiyun, LI Yi, et al. Reprogrammable logic in memristive crossbar for in-memory computing[J]. Journal of Physics D: Applied Physics, 2017, 50(50): 505102. doi: 10.1088/1361-6463/aa9646
[24]	KARIMI A and REZAI A. High-performance digital logic implementation approach using novel Memristor-based multiplexer[J]. International Journal of Circuit Theory and Applications, 2019, 47(12): 1933–1947. doi: 10.1002/cta.2712
[25]	BEN-HUR R, RONEN R, HAJ-ALI A, et al. SIMPLER MAGIC: Synthesis and mapping of in-memory logic executed in a single row to improve throughput[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, 39(10): 2434–2447. doi: 10.1109/TCAD.2019.2931188
[26]	LIU Xiaoxiao, MAO Mengjie, LIU Beiye, et al. Harmonica: A framework of heterogeneous computing systems with memristor-based neuromorphic computing accelerators[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2016, 63(5): 617–628. doi: 10.1109/TCSI.2016.2529279
[27]	HALAWANI Y, MOHAMMAD B, Al-QUTAYRI M, et al. A re-configurable memristor array structure for in-memory computing applications[C]. 2018 30th International Conference on Microelectronics (ICM), Sousse, Tunisia, 2018: 160–163. doi: 10.1109/ICM.2018.8704111.
[28]	ZHANG Xunming, ZHANG Quan, YANG Jianguo, et al. Novel hybrid computing architecture with memristor-based processing-in-memory for data-intensive applications[C]. 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Qingdao, China, 2018: 1–3. doi: 10.1109/ICSICT.2018.8564888.
[29]	ALAM M R, NAJAFI M H, and NEJAD N T. Exact stochastic computing multiplication in memristive memory[J]. IEEE Design & Test, To be published. doi: 10.1109/MDAT.2021.3051296
[30]	KOLMS T, LANG C, WALDNER A, et al. Towards in-memory computing: Arithmetic operations on real memristors[C]. The IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 2020: 2296–2301. doi: 10.1109/IECON43393.2020.9254441.
[31]	ZANOTTI T, PUGLISI F M, and PAVAN P. Reconfigurable smart in-memory computing platform supporting logic and binarized neural networks for low-power edge devices[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2020, 10(4): 478–487. doi: 10.1109/JETCAS.2020.3030542
[32]	CHENG Long, LI Yi, YIN Kangsheng, et al. Functional demonstration of a memristive arithmetic logic unit (MemALU) for in-memory computing[J]. Advanced Functional Materials, 2019, 29(49): 1905660. doi: 10.1002/adfm.201905660
[33]	DAI Lan, GUO Hong, LIN Qipeng, et al. An in-memory-computing design of multiplier based on multilevel-cell of resistance switching random access memory[J]. Chinese Journal of Electronics, 2018, 27(6): 1151–1157. doi: 10.1049/cje.2018.08.006
[34]	ZHOU Yaxiong, LI Yi, DUAN Nian, et al. Boolean and sequential logic in a one-memristor-one-resistor (1M1R) structure for in-memory computing[J]. Advanced Electronic Materials, 2018, 4(9): 1800229. doi: 10.1002/aelm.201800229
[35]	HALAWANI Y, MOHAMMAD B, LEBDEH M A, et al. ReRAM-based in-memory computing for search engine and neural network applications[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019, 9(2): 388–397. doi: 10.1109/JETCAS.2019.2909317
[36]	CHU M, KIM B, PARK S, et al. Neuromorphic hardware system for visual pattern recognition with memristor array and CMOS neuron[J]. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2410–2419. doi: 10.1109/TIE.2014.2356439
[37]	LI Can, BELKIN D, LI Yunning, et al. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks[J]. Nature Communications, 2018, 9(1): 2385. doi: 10.1038/s41467-018-04484-2
[38]	DUAN Qingxi, JING Zhaokun, ZOU Xiaolong, et al. Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks[J]. Nature Communications, 2020, 11(1): 3399. doi: 10.1038/s41467-020-17215-3
[39]	YAO Peng, WU Huaqiang, GAO Bin, et al. Fully hardware-implemented memristor convolutional neural network[J]. Nature, 2020, 577(7792): 641–646. doi: 10.1038/s41586-020-1942-4
[40]	BALAJI A, DAS A, WU Yuefeng, et al. Mapping spiking neural networks to neuromorphic hardware[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2020, 28(1): 76–86. doi: 10.1109/TVLSI.2019.2951493
[41]	TAN Hongwei, MAJUMDAR S, QIN Qihang, et al. Mimicking neurotransmitter release and long-term plasticity by oxygen vacancy migration in a tunnel junction memristor[J]. Advanced Intelligent Systems, 2019, 1(2): 1900036. doi: 10.1002/aisy.201900036
[42]	WIJESINGHE P, ANKIT A, SENGUPTA A, et al. An all-memristor deep spiking neural computing system: A step toward realizing the low-power stochastic brain[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(5): 345–358. doi: 10.1109/TETCI.2018.2829924
[43]	ZHANG Yang, WANG Xiaoping, and FRIEDMAN E G. Memristor-based circuit design for multilayer neural networks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2018, 65(2): 677–686. doi: 10.1109/TCSI.2017.2729787
[44]	ZHAO Yongyan, SUN Wuji, WANG Jia, et al. All‐inorganic ionic polymer-based memristor for high-performance and flexible artificial synapse[J]. Advanced Functional Materials, 2020, 30(39): 2004245. doi: 10.1002/adfm.202004245
[45]	GREENBERG-TOLEDO T, MAZOR R, HAJ-ALI A, et al. Supporting the momentum training algorithm using a memristor-based synapse[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2019, 66(4): 1571–1583. doi: 10.1109/TCSI.2018.2888538
[46]	SUN Shengyang, XU Hui, LI Jiwei, et al. Cascaded architecture for memristor crossbar array based larger-scale neuromorphic computing[J]. IEEE Access, 2019, 7: 61679–61688. doi: 10.1109/ACCESS.2019.2915787
[47]	HU Lingxiang, YANG Jing, WANG Jingrui, et al. All‐optically controlled memristor for optoelectronic neuromorphic computing[J]. Advanced Functional Materials, 2021, 31(4): 2005582. doi: 10.1002/adfm.202005582
[48]	LI Haoyang, HUANG Xiaodi, YUAN Junhui, et al. Controlled memory and threshold switching behaviors in a heterogeneous memristor for neuromorphic computing[J]. Advanced Electronic Materials, 2020, 6(8): 2000309. doi: 10.1002/aelm.202000309
[49]	BAE W and YOON K J. Weight update generation circuit utilizing phase noise of integrated complementary metal-oxide-semiconductor ring oscillator for memristor crossbar array neural network-based stochastic learning[J]. Advanced Intelligent Systems, 2020, 2(5): 2000011. doi: 10.1002/aisy.202000011
[50]	ZHU Bowen, WANG Hong, LIU Yaqing, et al. Skin-inspired haptic memory arrays with an electrically reconfigurable architecture[J]. Advanced Materials, 2016, 28(8): 1559–1566. doi: 10.1002/adma.201504754
[51]	ZHANG Chen, YE Wenbin, ZHOU Kui, et al. Bioinspired artificial sensory nerve based on nafion memristor[J]. Advanced Functional Materials, 2019, 29(20): 1808783. doi: 10.1002/adfm.201808783
[52]	JIANG Chengming, LI Qikun, SUN Nan, et al. A high-performance bionic pressure memory device based on piezo-OLED and piezo-memristor as luminescence-fish neuromorphic tactile system[J]. Nano Energy, 2020, 77: 105120. doi: 10.1016/j.nanoen.2020.105120
[53]	HE Ke, LIU Yaqing, WANG Ming, et al. An artificial somatic reflex arc[J]. Advanced Materials, 2020, 32(4): 1905399. doi: 10.1002/adma.201905399
[54]	LIU Fengyuan, TAUBE W, YOGESWARAN N, et al. Transforming the short-term sensing stimuli to long-term e-skin memory[C]. 2017 IEEE SENSORS, Glasgow, UK, 2017: 1–3. doi: 10.1109/ICSENS.2017.8234187.
[55]	SUN Yihui, ZHENG Xin, YAN Xiaoqin, et al. Bioinspired tribotronic resistive switching memory for self-powered memorizing mechanical stimuli[J]. ACS Applied Materials & Interfaces, 2017, 9(50): 43822–43829. doi: 10.1021/acsami.7b15269
[56]	WANG Zilu, HONG Qinghui, and WANG Xiaoping. Memristive circuit design of emotional generation and evolution based on skin-like sensory processor[J]. IEEE Transactions on Biomedical Circuits and Systems, 2019, 13(4): 631–644. doi: 10.1109/TBCAS.2019.2923055
[57]	WAN Changjin, CAI Pingqiang, GUO Xintong, et al. An artificial sensory neuron with visual-haptic fusion[J]. Nature Communications, 2020, 11: 4602. doi: 10.1038/s41467-020-18375-y
[58]	RAHMAN M A, WALIA S, NAZNEE S, et al. Artificial somatosensors: Feedback receptors for electronic skins[J]. Advanced Intelligent Systems, 2020, 2(11): 2000094. doi: 10.1002/aisy.202000094
[59]	LIU Haitao, HUA Qilin, YU Ruomeng, et al. A bamboo-like GaN microwire-based piezotronic memristor[J]. Advanced Functional Materials, 2016, 26(29): 5307–5314. doi: 10.1002/adfm.201600962
[60]	KIM Y, CHORTOS A, XU Wentao, et al. A bioinspired flexible organic artificial afferent nerve[J]. Science, 2018, 360(6392): 998–1003. doi: 10.1126/science.aao0098
[61]	EMBORAS A, GOYKHMAN I, DESIATOV B, et al. Nanoscale plasmonic memristor with optical readout functionality[J]. Nano Letters, 2013, 13(12): 6151–6155. doi: 10.1021/nl403486x
[62]	TAN Hongwei, LIU Gang, ZHU Xiaojian, et al. An optoelectronic resistive switching memory with integrated demodulating and arithmetic functions[J]. Advanced Materials, 2015, 27(17): 2797–2803. doi: 10.1002/adma.201500039
[63]	SEO S, JO S H, KIM S, et al. Artificial optic-neural synapse for colored and color-mixed pattern recognition[J]. Nature Communications, 2018, 9(1): 5106. doi: 10.1038/s41467-018-07572-5
[64]	ZHOU Feichi, ZHOU Zheng, CHEN Jiewei, et al. Optoelectronic resistive random access memory for neuromorphic vision sensors[J]. Nature Nanotechnology, 2019, 14(8): 776–782. doi: 10.1038/s41565-019-0501-3
[65]	YANG Lin, SINGH M, SHEN S W, et al. Transparent and flexible inorganic perovskite photonic artificial synapses with dual-mode operation[J]. Advanced Functional Materials, 2020, 31(6): 2008259. doi: 10.1002/adfm.202008259
[66]	LORENZI P, SUCRE V, ROMANO G, et al. Memristor based neuromorphic circuit for visual pattern recognition[C]. 2015 International Conference on Memristive Systems (MEMRISYS), Paphos, Cyprus, 2015: 1–2. doi: 10.1109/MEMRISYS.2015.7378387.
[67]	SARKAR M, CHOWDHURY A, ARKA A I, et al. A new supervised learning approach for visual pattern recognition using discrete circuit elements and memristor array[C]. The TENCON 2017 – 2017 IEEE Region 10 Conference, Penang, Malaysia, 2017: 223–228. doi: 10.1109/TENCON.2017.8227866.
[68]	ASCOLI A, MESSARIS I, TETZLAFF R, et al. CNNs with bistable-like non-volatile memristors: A novel mem-computing paradigm for the IoT era[C]. 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Bordeaux, France, 2018: 541–544. doi: 10.1109/ICECS.2018.8617924.
[69]	DONG Zhekang, LAI C S, HE Yufei, et al. Hybrid dual-CMOS/memristor synapse-based neural network with its applications in image super-resolution[J]. IET Circuits, Devices & Systems, 2019, 13(8): 1241–1248. doi: 10.1049/iet-cds.2018.5062
[70]	HALAWANI Y, MOHAMMAD B, AL-QUTAYRI M, et al. Memristor-based hardware accelerator for image compression[J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2018, 26(12): 2749–2758. doi: 10.1109/TVLSI.2018.2835572
[71]	WU Lindong, WANG Zongwei, WANG Bowen, et al. Emulation of biphasic plasticity in retinal electrical synapses for light-adaptive pattern pre-processing[J]. Nanoscale, 2021, 13(6): 3483–3492. doi: 10.1039/d0nr08012h
[72]	LIN Ya, WANG Cong, REN Yanyun, et al. Analog-digital hybrid memristive devices for image pattern recognition with tunable learning accuracy and speed[J]. Small Methods, 2019, 3(10): 1900160. doi: 10.1002/smtd.201900160
[73]	SUN Yilin, QIAN Liu, XIE Dan, et al. Photoelectric synaptic plasticity realized by 2D perovskite[J]. Advanced Functional Materials, 2019, 29(28): 1902538. doi: 10.1002/adfm.201902538
[74]	YANG C M, CHEN T C, VERMA D, et al. Bidirectional all-optical synapses based on a 2D Bi₂O₂Se/graphene hybrid structure for multifunctional optoelectronics[J]. Advanced Functional Materials, 2020, 30(30): 2001598. doi: 10.1002/adfm.202001598
[75]	WANG Xin, LU Yang, ZHANG Junyao, et al. Highly sensitive artificial visual array using transistors based on porphyrins and semiconductors[J]. Small, 2021, 17(2): 2005491. doi: 10.1002/smll.202005491
[76]	HSU H T, YANG Donglin, WIYANTO L D, et al. Red-light-stimulated photonic synapses based on non-volatile perovskite-based photomemory[J]. Advanced Photonics Research, 2021: 2000185. doi: 10.1002/adpr.202000185
[77]	ZHANG Lei, YU Hao, XIAO Cancheng, et al. Building light stimulated synaptic memory devices for visual memory simulation[J]. Advanced Electronic Materials, 2021, 7(1): 2000945. doi: 10.1002/aelm.202000945
[78]	DONG Zhekang, LAI C S, QI Donglian, et al. A general memristor-based pulse coupled neural network with variable linking coefficient for multi-focus image fusion[J]. Neurocomputing, 2018, 308: 172–183. doi: 10.1016/j.neucom.2018.04.066
[79]	NYENKE C and DONG Lixin. Sensing ambient oxygen using a W/CuxO/Cu memristor[C]. The 10th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Xi’an, China, 2015: 254–258. doi: 10.1109/NEMS.2015.7147421.
[80]	SHULAKER M M, HILLS G, PARK R S, et al. Three-dimensional integration of nanotechnologies for computing and data storage on a single chip[J]. Nature, 2017, 547(7661): 74–78. doi: 10.1038/nature22994
[81]	IWATA T, ONO K, YOSHIKAWA T, et al. Gas discrimination based on single-device extraction of transient sensor response by a MetalOxide memristor toward olfactory sensor array[C]. 2019 IEEE SENSORS, Montreal, Canada, 2019: 1–4. doi: 10.1109/SENSORS43011.2019.8956826.
[82]	ADEYEMO A, JABIR A, MATHEW J, et al. Reliable gas sensing with memristive array[C]. 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design (IOLTS), Thessaloniki, Greece, 2017: 244–246.
[83]	KHANDELWAL S, BALA A, GUPTA V, et al. Fault modeling and simulation of memristor based gas sensors[C]. 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS), Rhodes, Greece, 2019: 58–59. doi: 10.1109/IOLTS.2019.8854459.
[84]	WEN Changbao, HONG Jitong, RU Feng, et al. A novel memristor-based gas cumulative flow sensor[J]. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9531–9538. doi: 10.1109/TIE.2019.2891436
[85]	VIDIŠ M, PLECENIK T, MOŠKO M, et al. Gasistor: A memristor based gas-triggered switch and gas sensor with memory[J]. Applied Physics Letters, 2019, 115(9): 093504. doi: 10.1063/1.5099685
[86]	SHAH J, BARANGI M, and MAZUMDER P. Memristor crossbar memory for hybrid ultra low power hearing aid speech processor[C]. 2013 13th IEEE International Conference on Nanotechnology (IEEE-NANO 2013), Beijing, China, 2013: 83–86. doi: 10.1109/NANO.2013.6720867.
[87]	SALEH Q, MERKEL C, KUDITHIPUDI D, et al. Memristive computational architecture of an echo state network for real-time speech-emotion recognition[C]. 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), Verona, USA, 2015: 1–5. doi: 10.1109/CISDA.2015.7208624.
[88]	RAFIQUE M A, LEE B G, and JEON M. Hybrid neuromorphic system for automatic speech recognition[J]. Electronics Letters, 2016, 52(17): 1428–1430. doi: 10.1049/el.2016.0975
[89]	WANG Wei, PEDRETTI G, MILO V, et al. Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses[J]. Science Advances, 2018, 4(9): eaat4752. doi: 10.1126/sciadv.aat4752
[90]	SUN Linfeng, ZHANG Yishu, HWANG G, et al. Synaptic computation enabled by joule heating of single-layered semiconductors for sound localization[J]. Nano Letters, 2018, 18(4): 3229–3234. doi: 10.1021/acs.nanolett.8b00994
[91]	LI Can, HAN Lili, JIANG Hao, et al. Three-dimensional crossbar arrays of self-rectifying Si/SiO₂/Si memristors[J]. Nature Communications, 2017, 8: 15666. doi: 10.1038/ncomms15666
[92]	BISHOP M D, WONG H S P, MITRA S, et al. Monolithic 3-D integration[J]. IEEE Micro, 2019, 39(6): 16–27. doi: 10.1109/MM.2019.2942982
[93]	LIN Peng, LI Can, WANG Zhongrui, et al. Three-dimensional memristor circuits as complex neural networks[J]. Nature Electronics, 2020, 3(4): 225–232. doi: 10.1038/s41928-020-0397-9
[94]	WANG Tianyu, MENG Jialin, CHEN Lin, et al. Flexible 3D memristor array for binary storage and multi‐states neuromorphic computing applications[J]. InfoMat, 2021, 3(2): 212–221. doi: 10.1002/inf2.12158
[95]	AN Hongyu, EHSAN M A, ZHOU Zhen, et al. Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons[J]. Integration, 2019, 65: 273–281. doi: 10.1016/j.vlsi.2017.10.009
[96]	SUN W, CHOI S, KIM B, et al. Three-dimensional (3D) vertical resistive random-access memory (VRRAM) synapses for neural network systems[J]. Materials, 2019, 12(20): 3451. doi: 10.3390/ma12203451
[97]	FERNANDO B R, QI Yangjie, YAKOPCIC C, et al. 3D memristor crossbar architecture for a multicore neuromorphic system[C]. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020. doi: 10.1109/IJCNN48605.2020.9206929.