Search

Article

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Research progress of neuromorphic computation based on memcapacitors

Ren Kuan Zhang Ke-Jia Qin Xi-Zi Ren Huan-Xin Zhu Shou-Hui Yang Feng Sun Bai Zhao Yong Zhang Yong

Citation:

Research progress of neuromorphic computation based on memcapacitors

Ren Kuan, Zhang Ke-Jia, Qin Xi-Zi, Ren Huan-Xin, Zhu Shou-Hui, Yang Feng, Sun Bai, Zhao Yong, Zhang Yong
PDF
HTML
Get Citation
  • The rapid development of artificial intelligence (AI) requires one to speed up the development of the domain-specific hardware specifically designed for AI applications. The neuromorphic computing architecture consisting of synapses and neurons, which is inspired by the integrated storage and parallel processing of human brain, can effectively reduce the energy consumption of artificial intelligence in computing work. Memory components have shown great application value in the hardware implementation of neuromorphic computing. Compared with traditional devices, the memristors used to construct synapses and neurons can greatly reduce computing energy consumption. However, in neural networks based on memristors, updating and reading operations have system energy loss caused by voltage and current of memristors. As a derivative of memristor, memcapacitor is considered as a potential device to realize a low energy consumption neural network, which has attracted wide attention from academia and industry. Here, we review the latest advances in physical/simulated memcapacitors and their applications in neuromorphic computation, including the current principle and characteristics of physical/simulated memcapacitor, representative synapses, neurons and neuromorphic computing architecture based on memcapacitors. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic computation based on memcapacitors.
      Corresponding author: Yang Feng, yf@swjtu.edu.cn ; Zhang Yong, yongzhang@swjtu.edu.cn
    • Funds: Project supported by the National High Technology Research and Development Program of China (Grant No. 2017YFE0301401)
    [1]

    Goodfellow I, Bengio Y, Courville A 2016 Deep Learning (Cambridge: The MIT Press) pp1−100

    [2]

    James C D, Aimone J B, Miner N E, Vineyard C M, Rothganger F H, Carlson K D, Mulder S A, Draelos T J, Faust A, Marinella M J, Naegle J H, Plimpton S J 2017 Biol. Inspired Cogn. Archit. 19 49Google Scholar

    [3]

    Merolla P A, Arthur J V, Alvarez-Icaza R, Cassidy A S, Sawada J, Akopyan F, Jackson B L, Imam N, Guo C, Nakamura Y, Brezzo B, Vo I, Esser S K, Appuswamy R, Taba B, Amir A, Flickner M D, Risk W P, Manohar R, Modha D S 2014 Science 345 668Google Scholar

    [4]

    Furber S B, Galluppi F, Temple S, Plana L A 2014 Proc. IEEE 102 652Google Scholar

    [5]

    Chua L 1971 IEEE Trans. Circuit Theory 18 507Google Scholar

    [6]

    Strukov D B, Snider G S, Stewart D R, Williams R S 2008 Nature 453 80Google Scholar

    [7]

    Dev D, Krishnaprasad A, Shawkat M S, He Z, Das S, Fan D, Chung H S, Jung Y, Roy T 2020 IEEE Electron Device Lett. 41 936Google Scholar

    [8]

    He C, Tang J, Shang D S, Tang J, Xi Y, Wang S, Li N, Zhang Q, Lu J K, Wei Z, Wang Q, Shen C, Li J, Shen S, Shen J, Yang R, Shi D, Wu H, Wang S, Zhang G 2020 ACS Appl. Mater. Interfaces 12 11945Google Scholar

    [9]

    Wang H, Yan X B, Zhao M L, Zhao J H, Zhou Z Y, Wang J J, Hao W C 2020 Appl. Phys. Lett. 116 093501Google Scholar

    [10]

    Chen J R, Wu H Q, Gao B, Tang J S, Hu X B S, Qian H 2020 IEEE Trans. Electron Devices 67 2213Google Scholar

    [11]

    Liao Y, Gao B, Xu F, Yao P, Chen J R, Zhan W Q, Tang J S, Wu H Q, Qian H 2020 IEEE Trans. Electron Devices 67 1593Google Scholar

    [12]

    Yao P, Wu H, Gao B, Tang J, Zhang Q, Zhang W, Yang J J, Qian H 2020 Nature 577 641Google Scholar

    [13]

    Li X, Tang J, Zhang Q, Gao B, Yang J J, Song S, Wu W, Zhang W, Yao P, Deng N, Deng L, Xie Y, Qian H, Wu H 2020 Nat. Nanotechnol. 15 776Google Scholar

    [14]

    Di Ventra M, Pershin Y V, Chua L O 2009 Proc. IEEE 97 1717Google Scholar

    [15]

    Flak J 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications Turin, Italy, Aug. 29−31 2012 p1

    [16]

    Fouda M E, Radwan A G 2014 26th International Conference on Microelectronics (ICM) Doha, Qatar, Dec. 14−17 2014 p172

    [17]

    Pershin Y V, Di Ventra M 2014 Electron. Lett. 50 141Google Scholar

    [18]

    Yi S, ZhenZhen J, XiaoPing W, Yang L 2015 34th Chinese Control Conference (CCC) Hangzhou, China, July 28–30 2015 p3452

    [19]

    Tran S J D, Teuscher C 2017 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) Newport, RI, July 25−26 2017 p115

    [20]

    Wang Z, Rao M, Han J W, Zhang J, Lin P, Li Y, Li C, Song W, Asapu S, Midya R, Zhuo Y, Jiang H, Yoon J H, Upadhyay N K, Joshi S, Hu M, Strachan J P, Barnell M, Wu Q, Wu H, Qiu Q, Williams R S, Xia Q, Yang J J 2018 Nat Commun. 9 3208Google Scholar

    [21]

    Chen Y, Zhang J, Zhang Y, Zhang R, Kimura M, Nakashima Y 2019 17th IEEE International New Circuits and Systems Conference (NEWCAS) Munich, Germany, June 23−26 2019 p1

    [22]

    Tran S J D, Teuscher C 2019 IEEE International Conference on Rebooting Computing (ICRC) San Mateo, CA, Nov. 6−8 2019 p110

    [23]

    L.Chua 2015 Radioengineering 24 319Google Scholar

    [24]

    Bessonov A A, Kirikova M N, Petukhov D I, Allen M, Ryhanen T, Bailey M J 2015 Nat. Mater. 14 199Google Scholar

    [25]

    Goswami S, Rath S P, Thompson D, Hedstrom S, Annamalai M, Pramanick R, Ilic B R, Sarkar S, Hooda S, Nijhuis C A, Martin J, Williams R S, Goswami S, Venkatesan T 2020 Nat. Nanotechnol. 15 380Google Scholar

    [26]

    Lai Q X, Zhang L, Li Z Y, Stickle W F, Williams R S, Chen Y 2009 Appl. Phys. Lett. 95 213503Google Scholar

    [27]

    Liu R X, Dong R X, Qin S C, Yan X L 2020 Org. Electron. 81 105680Google Scholar

    [28]

    Liu S Q, Wu N J, Ignatiev A, Li J R 2006 J. Appl. Phys. 100 056101Google Scholar

    [29]

    Martinez-Rincon J, Di Ventra M, Pershin Y V 2010 Phys. Rev. B. 81 195430Google Scholar

    [30]

    Najem J S, Hasan M S, Williams R S, Weiss R J, Rose G S, Taylor G J, Sarles S A, Collier C P 2019 Nat Commun. 10 3239Google Scholar

    [31]

    Nieminen H, Ermolov V, Nybergh K, Silanto S, Ryhanen T 2002 J. Micromech. Microeng. 12 177Google Scholar

    [32]

    Noh Y J, Baek Y J, Hu Q, Kang C J, Choi Y J, Lee H H, Yoon T S 2015 IEEE Trans. Nanotechnol. 14 798Google Scholar

    [33]

    Park D, Yang P, Kim H J, Beom K, Lee H H, Kang C J, Yoon T S 2018 Appl. Phys. Lett. 113 162102Google Scholar

    [34]

    Román Acevedo W, van den Bosch C A M, Aguirre M H, Acha C, Cavallaro A, Ferreyra C, Sánchez M J, Patrone L, Aguadero A, Rubi D 2020 Appl. Phys. Lett. 116 063502Google Scholar

    [35]

    Salaoru I, Khiat A, Li Q J, Berdan R, Prodromakis T 2013 Appl. Phys. Lett. 103 233513Google Scholar

    [36]

    Slesazeck S, Wylezich H, Mikolajick T 2017 IEEE 8th Latin American Symposium on Circuits & Systems (LASCAS) Bariloche, Argentina, Feb. 20−23 2017 p1

    [37]

    Sun J, Lind E, Maximov I, Xu H Q 2011 IEEE Electron Device Lett. 32 131Google Scholar

    [38]

    Wu S X, Peng H Y, Wu T 2011 Appl. Phys. Lett. 98 093503Google Scholar

    [39]

    Ahmed M G, Cho K, Cho T 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications Turin, Italy, Aug. 29−31 2012 p1

    [40]

    Asapu S, Pershin Y V 2015 IEEE Trans. Electron Devices 62 3678Google Scholar

    [41]

    Biolek D, Biolek Z, Biolkova V 2009 European Conference on Circuit Theory and Design Antalya, Turkey, Aug. 23−27 2009 p249

    [42]

    Biolek D, Biolek Z, Biolkova V 2010 Electron. Lett. 46 520Google Scholar

    [43]

    Biolek D, Biolkova V 2010 Electron. Lett. 46 1428Google Scholar

    [44]

    Biolek D, Biolková V, Kolka Z 2010 IEEE Asia Pacific Conference on Circuits and Systems Kuala Lumpur, Malaysia Dec. 6−9 2010 p800

    [45]

    Flak J, Raantala A, Haatainen T, Prunnila M, Laiho M 2014 14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA) Notre Dame, IN, USA, July 29−31 2014 p1

    [46]

    Fouda M E, Radwan A G 2012 Electron. Lett. 48 1454Google Scholar

    [47]

    Pershin Y V, Di Ventra M 2010 Electron. Lett. 46 517Google Scholar

    [48]

    Pershin Y V, Di Ventra M 2011 Electron. Lett. 47 243Google Scholar

    [49]

    Romero F J, Morales D P, Godoy A, Ruiz F G, Tienda-Luna I M, Ohata A, Rodriguez N 2019 Int. J. Circ. Theor. App. 47 572Google Scholar

    [50]

    Yu D S, Liang Y, Iu H H C, Chua L O 2014 IEEE Trans. Circuits Syst. II-Express Briefs 61 758Google Scholar

    [51]

    Yu D, Zhao X, Sun T, Iu H H C, Fernando T 2020 IEEE Trans. Circuits Syst. II-Express Briefs 67 1334Google Scholar

    [52]

    Yu D, Zhou Z, Iu H H C, Fernando T, Hu Y 2016 IEEE Trans. Circuits Syst. II-Express Briefs 63 1101Google Scholar

    [53]

    Yu D S, Liang Y, Chen H, Iu H H C 2013 IEEE Trans. Circuits Syst. II-Express Briefs 60 207Google Scholar

    [54]

    Zheng C Y, Yu D S, Iu H H C, Fernando T, Sun T T, Eshraghian J K, Guo H D 2019 IEEE Trans. Circuits Syst. I-Regul. Pap. 66 4793Google Scholar

    [55]

    Kwon D, Chung I Y 2020 IEEE Electron Device Lett. 41 493Google Scholar

    [56]

    Zhao L, Fan Z, Cheng S L, Hong L Q, Li Y Q, Tian G, Chen D Y, Hou Z P, Qin M H, Zeng M, Lu X B, Zhou G F, Gao X S, Liu J M 2020 Adv Electron Mater 6 1900858Google Scholar

    [57]

    Yamaletdinov R D, Ivakhnenko O V, Sedelnikova O V, Shevchenko S N, Pershin Y V 2018 Sci. Rep. 8 3566Google Scholar

    [58]

    Patel J A, Sandhie Z T, Chowdhury M H 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) Windsor, Canada, Aug. 5–8 2018 p1130

    [59]

    Salaoru I, Li Q, Khiat A, Prodromakis T 2014 Nanoscale. Res. Lett. 9 552Google Scholar

    [60]

    Cai J W, Li L X, Xu C, Feng Y, Zhong Y N, Xu J L, Gao X, Wang S D 2019 Appl. Phys. Lett. 114 043302Google Scholar

    [61]

    Qian W H, Cheng X F, Zhao Y Y, Zhou J, He J H, Li H, Xu Q F, Li N J, Chen D Y, Lu J M 2019 Adv. Mater. 31 1806424Google Scholar

    [62]

    Yang P, Jun Kim H, Zheng H, Won Beom G, Park J S, Jung Kang C, Yoon T S 2017 Nanotechnology 28 225201Google Scholar

    [63]

    Martinez-Rincon J, Pershin Y V 2011 IEEE Trans. Electron Devices 58 1809Google Scholar

    [64]

    Yang C, Yang N, Yu Y, Li Y, Diez F F 2017 IEEE 17th International Conference on Communication Technology (ICCT) Chengdu, China, Oct. 27–30 2017 p1171

    [65]

    Corinto F, Di Marco M, Forti M, Chua L 2019 IEEE Trans Cybern 50 4758Google Scholar

    [66]

    Cohen G Z, Pershin Y V, Di Ventra M 2012 Phys. Rev. B. 85 165428Google Scholar

    [67]

    Mcculloch W S, Pitts W 1943 Bull. Math. Biol. 5 115

    [68]

    Hodgkin A L, Huxley A F 1989 Bull. Math. Biol. 52 25

    [69]

    Pershin Y V, Di Ventra M 2011 Adv. Phys. 60 145Google Scholar

    [70]

    Rumelhart D E, Hinton G E, Williams R J 1986 Nature 323 533Google Scholar

    [71]

    John H, Anders K, Palmer R G 1991 Phys. Today 44 70

    [72]

    Bi G Q, Poo M M 2001 Annu. Rev. Neurosci. 24 139Google Scholar

  • 图 1  (a)忆容系统的捏滞曲线[14]; (b)仿真的压控忆容器q-V曲线[14]; (c) 仿真的压控忆容器C-V曲线[14]

    Figure 1.  (a) Schematics of a pinched hysteresis loop of a memcapacitive system[14]; (b) q-V curve of a simulated voltage-controlled memcapacitor[14]; (c) C-V curve of a simulated voltage-controlled memcapacitor[14].

    图 2  基于忆容的神经形态计算

    Figure 2.  Neuromorphic computation based on memcapacitors.

    图 3  ITO (In-Sn-O)/HfOx/p-Si结构忆容器及C-V曲线[33]

    Figure 3.  Structure of ITO (In-Sn-O)/HfOx/p-S memcapacitor and its C-V curves[33].

    图 4  Au/Ti/HfOx/InP结构忆容器[37] (a) 器件结构及总I-V曲线; (b) 零偏压下器件能带结构; (c) 器件$ {\rm{R}}{\rm{C}} $等效电路及C-V曲线; (d) 器件R-V曲线

    Figure 4.  Structure of Au/Ti/HfOx/InP memcapacitor[37]: (a) device structure and total I-V curves; (b) schematics for the band diagram of the metal HfO2-semiconductordiode at zero bias; (c) equivalent circuit of device and its C-V curves; (d) R-V curves.

    图 5  室温下Pt/Pr0.7Ca0.3MnO3(PCMO)/YBCO/LAO结构忆容器性质[28] (a) 非易失电容随脉冲电压数的变化; (b) 非易失电阻随脉冲电压数的变化; (c) 非易失电容随测试电压频率的变化

    Figure 5.  Nonvolatile capacitance and resistance changes for Au/PCMO/YBCO/LAO structure sample at room temperature[28]: (a) Nonvolatile capacitance changes with applied pulse numbers; (b) nonvolatile resistance changes with applied pulse numbers; (c) nonvolatile capacitance changes with frequency.

    图 6  (a) Ag(TE)/MoOx/MoS2/Ag(BE)忆容器件结构[24]; (b)钼氧化态MoOx/MoS2样品在200 ℃持续3 h退火后的XPS剖面; 填充区域代表一个Mo6 +丰富的区域[24]; (c)电阻、电容开关性质[24]

    Figure 6.  [24](a) Ag(TE)/MoOx/MoS2/Ag(BE) structure memcapacitor[24]; (b) Molybdenum oxidation-state XPS profile of the MoOx/MoS2 sample annealed at 200 ℃ for 3 h; the filled area represents a Mo6+ -rich region[24]; (c) capacitance and resistance switch characteristics[24].

    图 7  (a)Al/Ti/RbAg4I5 /MEH-PPV/SiO2 /p-Si/Al忆容器结构及其特性曲线[26]; (b) ITO/MASnBr3/Au结构图[61]; (c) ITO/MASnBr3/Au原理图[61]; (d) ITO/MASnBr3/Au结构的忆容特性(1 MHz下的C-VQ-V特性)[61]; (e) 硅底电极保持接地的有机薄膜记忆电容的器件结构和正负偏压下的电荷积累方案[60]

    Figure 7.  (a) A memory capacitor with an Al/Ti/RbAg4I5 /MEH-PPV/SiO2 /p-Si/Al structure (inset) and its characteristic curve[26]; (b) schematic diagram of the ITO/MASnBr3/Au structure[61]; (c) mechanism of the ITO/MASnBr3/Au structure[61]; (d) memcapacitive characteristics of the ITO/MASnBr3/Au device(C-V hysteresis and Q-V loops detected at 1 MHz); (e) device structure and charge accumulation scheme under negative (top) and positive (bottom) biases of an organic thin film memcapacitor, where the Si bottom electrode is kept grounded[60].

    图 8  基于记忆电容的人工突触短期塑性模拟[27] (a) 生物突触和Al/共聚物/ITO人工突触装置信号传输示意图, 共聚物薄膜的AFM图像; (b) C-V曲线; (c) 器件的PPF行为, A1和A2分别代表第一个和第二个突触前突起的PSC, 红色和蓝色曲线分别代表正、负电压下的兴奋性PSC和抑制性PSC; (d) PPF指数被绘制成时间间隔的函数

    Figure 8.  Short-term plasticity emulated in artificial synapse based on memory capacitance[27]: (a) Schematic illustrations of the signal transmission in biological synapse and the Al/copolymer/ITO artificial synaptic device. AFM image of copolymer film; (b) the C-V curves; (c) PPF behaviors of the device. A1 and A2 represent the PSC of the first and second presynaptic spike, respectively. The red and blue curves represent the excitatory and inhibitory PSC under negative and positive voltage, respectively. The inset shows schematic of pulse application; (d)PPF index plotted as a function of the time interval.

    图 9  带[Ru(L)2](PF6)2层器件的测试结构及电学特性[25] (a) 3种结构的示意图; (b)—(d) A(b), B (c)和C (d)结构电流密度对电压J(V)的特性; (e)—(f) 顶部面板显示了结构A(e)和B(f)的相对介电常数与电压的特性, 并覆盖了相应结构的J(V)曲线; 底部的面板显示了结构A(e)和B(f)对应的电荷和电压曲线

    Figure 9.  Test structures and electrical characterizations of devices with [Ru(L)2](PF6)[25]: (a) Schematic illustration of the three structures; (b)–(d) the current density versus voltage J(V) characteristics of structures A(b), B(c) and C(d); (e)–(f) the top panels show the relative permittivity versus voltage characteristics of structures A(e) and B(f), overlaid with the J(V) curves of the corresponding structures. The bottom panels show the corresponding charge versus voltage profiles for structures A(e) and B(f).

    图 10  电子隧穿忆容模型[29]

    Figure 10.  Scheme of an electron tunneling memcapacitor[29].

    图 11  (a) 双状态MEM忆容[31]; (b) 忆容的电容-电压曲线[31]; (c) 弹性电极忆容

    Figure 11.  (a) Photograph of a two-state MEM capacitor[31]; (b) measured capacitance as a function of voltage of the two-state capacitor[31]; (c) elastic poles memcapacitor.

    图 12  生物忆容器仿生膜组装与电行为[30] (a) 一种模拟生物膜结构的电容平面脂质双分子层, 在脂质包被的微滴之间接触并排除多余油脂后自发形成; (b) 由静膜电压v(t)引起的几何变化示意图

    Figure 12.  Biomimetic membrane assembly and electromechanical behaviours[30]: (a) A capacitive planar lipid bilayer that mimics the structure of a biological membrane forms spontaneously upon contact between lipid-coated droplets and exclusion of excess oil; (b) a schematic describing the geometrical changes caused by a net membrane voltage, v(t).

    图 13  伪忆容[20] (a) 伪忆容的扫描电子显微图的平面视图和透射电子显微图的截面图; (b)集成伪忆容的电荷-电压关系

    Figure 13.  Dynamic pseudo-memcapacitor(DPM)[20]: (a) a scanning electron micrograph of the plan view of the integrated DPM, and a transmission electron micrograph of the cross-section; (b) charge-voltage relationship of the integrated DPM.

    图 14  忆容仿真电路原理图 (a) 基于密勒效应的忆容仿真电路[49]; (b) 忆容-电阻串联电路[47]; (c) 提出的多功能电路[51]

    Figure 14.  Schematic of the memcapacitor emulator: (a) Schematic of the memcapacitor emulator based on the Miller effect[49]; (b) memcapacitor-resistor series circuit[47]; (c) the proposed mutator circuit[51].

    图 15  MP神经元模型

    Figure 15.  MP-neuron mode.

    图 16  忆容桥式突触电路[16]

    Figure 16.  Memcapacitor bridge synaptic circuit[16].

    图 17  忆容记忆突触[17] (a) 集成神经网络中的忆容突触; (b) 忆容突触实现STDP; (c) 单个突触的集成忆容神经元点火仿真

    Figure 17.  Memcapacitive synapses[17] (a) Memcapacitive synapses in integrate-and-fire neural network; (b) STDP with memcapacitive synapses; (c) simulation of integrate-and-fire memcapacitive network with only one spiking neuron.

    图 18  伪忆容突触[20] (a) 生物神经元接受高频突触后输入后产生动作电位的示意图; (b) 伪忆容的集成和触发过程; (c) 电子神经元晶体管的原理图; (d) 电子神经元-晶体管集成-点火过程的动力学

    Figure 18.  Pseudo-memcapacitor synapse[20]: (a) Schematic representation of a biological neuron generating an action potential after receiving high-frequency post-synaptic inputs; (b) the integrate-and-fire process of a pseudo-memcapacitor synapse; (c) schematic of the synapse-transistor; (d) dynamics of the synapse-transistor integrate-and-fire process.

    图 19  忆容-MOS耦合神经元胞体[21]

    Figure 19.  Neuron-MOS transistor couples the memcapacitor cells[21].

    图 20  MC-ACU[21] (a) 全结构电路图; (b) sigmoid神经元电路; (c) 在HSPICE中的仿真曲线(蓝)与理论数学曲线(红)对比; (d) 线性神经元电路; (e)在HSPICE中的仿真曲线(蓝)与理论数学曲线(红)对比

    Figure 20.  MC-ACU[21]: (a) Overall architecture; (b) sigmoid neuron circuit; (c) simulation results in HSPICE (blue) compared with the mathematical sigmoid(red); (d) linear neuron circuit; (e) simulation results in HSPICE (blue) compared with the mathematical linear (red).

    图 21  基于电容式网络的联想学习机制[20]. 两个突触前信号分别模拟食物的视觉和铃声. 突触后神经元模拟狗的唾液分泌. 与“食物”突触前神经元连接的突触的初始权重较大, 而与“钟”突触前神经元连接的突触的初始权重较小

    Figure 21.  Capacitive network for associative learning based on the Hebbian-like mechanism[20]. Two pre-synaptic signals model the sight of food and the sound of a bell, respectively. The post-synaptic neuron models the salivation of a dog. The initial weight of the synapse interfacing with the “food” pre-synaptic neuron was large, while that of the synapse connected to the “bell” pre-synaptic neuron was small

    图 22  忆容储层计算网络[22]

    Figure 22.  A memcapacitive reservoir network[22].

  • [1]

    Goodfellow I, Bengio Y, Courville A 2016 Deep Learning (Cambridge: The MIT Press) pp1−100

    [2]

    James C D, Aimone J B, Miner N E, Vineyard C M, Rothganger F H, Carlson K D, Mulder S A, Draelos T J, Faust A, Marinella M J, Naegle J H, Plimpton S J 2017 Biol. Inspired Cogn. Archit. 19 49Google Scholar

    [3]

    Merolla P A, Arthur J V, Alvarez-Icaza R, Cassidy A S, Sawada J, Akopyan F, Jackson B L, Imam N, Guo C, Nakamura Y, Brezzo B, Vo I, Esser S K, Appuswamy R, Taba B, Amir A, Flickner M D, Risk W P, Manohar R, Modha D S 2014 Science 345 668Google Scholar

    [4]

    Furber S B, Galluppi F, Temple S, Plana L A 2014 Proc. IEEE 102 652Google Scholar

    [5]

    Chua L 1971 IEEE Trans. Circuit Theory 18 507Google Scholar

    [6]

    Strukov D B, Snider G S, Stewart D R, Williams R S 2008 Nature 453 80Google Scholar

    [7]

    Dev D, Krishnaprasad A, Shawkat M S, He Z, Das S, Fan D, Chung H S, Jung Y, Roy T 2020 IEEE Electron Device Lett. 41 936Google Scholar

    [8]

    He C, Tang J, Shang D S, Tang J, Xi Y, Wang S, Li N, Zhang Q, Lu J K, Wei Z, Wang Q, Shen C, Li J, Shen S, Shen J, Yang R, Shi D, Wu H, Wang S, Zhang G 2020 ACS Appl. Mater. Interfaces 12 11945Google Scholar

    [9]

    Wang H, Yan X B, Zhao M L, Zhao J H, Zhou Z Y, Wang J J, Hao W C 2020 Appl. Phys. Lett. 116 093501Google Scholar

    [10]

    Chen J R, Wu H Q, Gao B, Tang J S, Hu X B S, Qian H 2020 IEEE Trans. Electron Devices 67 2213Google Scholar

    [11]

    Liao Y, Gao B, Xu F, Yao P, Chen J R, Zhan W Q, Tang J S, Wu H Q, Qian H 2020 IEEE Trans. Electron Devices 67 1593Google Scholar

    [12]

    Yao P, Wu H, Gao B, Tang J, Zhang Q, Zhang W, Yang J J, Qian H 2020 Nature 577 641Google Scholar

    [13]

    Li X, Tang J, Zhang Q, Gao B, Yang J J, Song S, Wu W, Zhang W, Yao P, Deng N, Deng L, Xie Y, Qian H, Wu H 2020 Nat. Nanotechnol. 15 776Google Scholar

    [14]

    Di Ventra M, Pershin Y V, Chua L O 2009 Proc. IEEE 97 1717Google Scholar

    [15]

    Flak J 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications Turin, Italy, Aug. 29−31 2012 p1

    [16]

    Fouda M E, Radwan A G 2014 26th International Conference on Microelectronics (ICM) Doha, Qatar, Dec. 14−17 2014 p172

    [17]

    Pershin Y V, Di Ventra M 2014 Electron. Lett. 50 141Google Scholar

    [18]

    Yi S, ZhenZhen J, XiaoPing W, Yang L 2015 34th Chinese Control Conference (CCC) Hangzhou, China, July 28–30 2015 p3452

    [19]

    Tran S J D, Teuscher C 2017 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) Newport, RI, July 25−26 2017 p115

    [20]

    Wang Z, Rao M, Han J W, Zhang J, Lin P, Li Y, Li C, Song W, Asapu S, Midya R, Zhuo Y, Jiang H, Yoon J H, Upadhyay N K, Joshi S, Hu M, Strachan J P, Barnell M, Wu Q, Wu H, Qiu Q, Williams R S, Xia Q, Yang J J 2018 Nat Commun. 9 3208Google Scholar

    [21]

    Chen Y, Zhang J, Zhang Y, Zhang R, Kimura M, Nakashima Y 2019 17th IEEE International New Circuits and Systems Conference (NEWCAS) Munich, Germany, June 23−26 2019 p1

    [22]

    Tran S J D, Teuscher C 2019 IEEE International Conference on Rebooting Computing (ICRC) San Mateo, CA, Nov. 6−8 2019 p110

    [23]

    L.Chua 2015 Radioengineering 24 319Google Scholar

    [24]

    Bessonov A A, Kirikova M N, Petukhov D I, Allen M, Ryhanen T, Bailey M J 2015 Nat. Mater. 14 199Google Scholar

    [25]

    Goswami S, Rath S P, Thompson D, Hedstrom S, Annamalai M, Pramanick R, Ilic B R, Sarkar S, Hooda S, Nijhuis C A, Martin J, Williams R S, Goswami S, Venkatesan T 2020 Nat. Nanotechnol. 15 380Google Scholar

    [26]

    Lai Q X, Zhang L, Li Z Y, Stickle W F, Williams R S, Chen Y 2009 Appl. Phys. Lett. 95 213503Google Scholar

    [27]

    Liu R X, Dong R X, Qin S C, Yan X L 2020 Org. Electron. 81 105680Google Scholar

    [28]

    Liu S Q, Wu N J, Ignatiev A, Li J R 2006 J. Appl. Phys. 100 056101Google Scholar

    [29]

    Martinez-Rincon J, Di Ventra M, Pershin Y V 2010 Phys. Rev. B. 81 195430Google Scholar

    [30]

    Najem J S, Hasan M S, Williams R S, Weiss R J, Rose G S, Taylor G J, Sarles S A, Collier C P 2019 Nat Commun. 10 3239Google Scholar

    [31]

    Nieminen H, Ermolov V, Nybergh K, Silanto S, Ryhanen T 2002 J. Micromech. Microeng. 12 177Google Scholar

    [32]

    Noh Y J, Baek Y J, Hu Q, Kang C J, Choi Y J, Lee H H, Yoon T S 2015 IEEE Trans. Nanotechnol. 14 798Google Scholar

    [33]

    Park D, Yang P, Kim H J, Beom K, Lee H H, Kang C J, Yoon T S 2018 Appl. Phys. Lett. 113 162102Google Scholar

    [34]

    Román Acevedo W, van den Bosch C A M, Aguirre M H, Acha C, Cavallaro A, Ferreyra C, Sánchez M J, Patrone L, Aguadero A, Rubi D 2020 Appl. Phys. Lett. 116 063502Google Scholar

    [35]

    Salaoru I, Khiat A, Li Q J, Berdan R, Prodromakis T 2013 Appl. Phys. Lett. 103 233513Google Scholar

    [36]

    Slesazeck S, Wylezich H, Mikolajick T 2017 IEEE 8th Latin American Symposium on Circuits & Systems (LASCAS) Bariloche, Argentina, Feb. 20−23 2017 p1

    [37]

    Sun J, Lind E, Maximov I, Xu H Q 2011 IEEE Electron Device Lett. 32 131Google Scholar

    [38]

    Wu S X, Peng H Y, Wu T 2011 Appl. Phys. Lett. 98 093503Google Scholar

    [39]

    Ahmed M G, Cho K, Cho T 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications Turin, Italy, Aug. 29−31 2012 p1

    [40]

    Asapu S, Pershin Y V 2015 IEEE Trans. Electron Devices 62 3678Google Scholar

    [41]

    Biolek D, Biolek Z, Biolkova V 2009 European Conference on Circuit Theory and Design Antalya, Turkey, Aug. 23−27 2009 p249

    [42]

    Biolek D, Biolek Z, Biolkova V 2010 Electron. Lett. 46 520Google Scholar

    [43]

    Biolek D, Biolkova V 2010 Electron. Lett. 46 1428Google Scholar

    [44]

    Biolek D, Biolková V, Kolka Z 2010 IEEE Asia Pacific Conference on Circuits and Systems Kuala Lumpur, Malaysia Dec. 6−9 2010 p800

    [45]

    Flak J, Raantala A, Haatainen T, Prunnila M, Laiho M 2014 14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA) Notre Dame, IN, USA, July 29−31 2014 p1

    [46]

    Fouda M E, Radwan A G 2012 Electron. Lett. 48 1454Google Scholar

    [47]

    Pershin Y V, Di Ventra M 2010 Electron. Lett. 46 517Google Scholar

    [48]

    Pershin Y V, Di Ventra M 2011 Electron. Lett. 47 243Google Scholar

    [49]

    Romero F J, Morales D P, Godoy A, Ruiz F G, Tienda-Luna I M, Ohata A, Rodriguez N 2019 Int. J. Circ. Theor. App. 47 572Google Scholar

    [50]

    Yu D S, Liang Y, Iu H H C, Chua L O 2014 IEEE Trans. Circuits Syst. II-Express Briefs 61 758Google Scholar

    [51]

    Yu D, Zhao X, Sun T, Iu H H C, Fernando T 2020 IEEE Trans. Circuits Syst. II-Express Briefs 67 1334Google Scholar

    [52]

    Yu D, Zhou Z, Iu H H C, Fernando T, Hu Y 2016 IEEE Trans. Circuits Syst. II-Express Briefs 63 1101Google Scholar

    [53]

    Yu D S, Liang Y, Chen H, Iu H H C 2013 IEEE Trans. Circuits Syst. II-Express Briefs 60 207Google Scholar

    [54]

    Zheng C Y, Yu D S, Iu H H C, Fernando T, Sun T T, Eshraghian J K, Guo H D 2019 IEEE Trans. Circuits Syst. I-Regul. Pap. 66 4793Google Scholar

    [55]

    Kwon D, Chung I Y 2020 IEEE Electron Device Lett. 41 493Google Scholar

    [56]

    Zhao L, Fan Z, Cheng S L, Hong L Q, Li Y Q, Tian G, Chen D Y, Hou Z P, Qin M H, Zeng M, Lu X B, Zhou G F, Gao X S, Liu J M 2020 Adv Electron Mater 6 1900858Google Scholar

    [57]

    Yamaletdinov R D, Ivakhnenko O V, Sedelnikova O V, Shevchenko S N, Pershin Y V 2018 Sci. Rep. 8 3566Google Scholar

    [58]

    Patel J A, Sandhie Z T, Chowdhury M H 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS) Windsor, Canada, Aug. 5–8 2018 p1130

    [59]

    Salaoru I, Li Q, Khiat A, Prodromakis T 2014 Nanoscale. Res. Lett. 9 552Google Scholar

    [60]

    Cai J W, Li L X, Xu C, Feng Y, Zhong Y N, Xu J L, Gao X, Wang S D 2019 Appl. Phys. Lett. 114 043302Google Scholar

    [61]

    Qian W H, Cheng X F, Zhao Y Y, Zhou J, He J H, Li H, Xu Q F, Li N J, Chen D Y, Lu J M 2019 Adv. Mater. 31 1806424Google Scholar

    [62]

    Yang P, Jun Kim H, Zheng H, Won Beom G, Park J S, Jung Kang C, Yoon T S 2017 Nanotechnology 28 225201Google Scholar

    [63]

    Martinez-Rincon J, Pershin Y V 2011 IEEE Trans. Electron Devices 58 1809Google Scholar

    [64]

    Yang C, Yang N, Yu Y, Li Y, Diez F F 2017 IEEE 17th International Conference on Communication Technology (ICCT) Chengdu, China, Oct. 27–30 2017 p1171

    [65]

    Corinto F, Di Marco M, Forti M, Chua L 2019 IEEE Trans Cybern 50 4758Google Scholar

    [66]

    Cohen G Z, Pershin Y V, Di Ventra M 2012 Phys. Rev. B. 85 165428Google Scholar

    [67]

    Mcculloch W S, Pitts W 1943 Bull. Math. Biol. 5 115

    [68]

    Hodgkin A L, Huxley A F 1989 Bull. Math. Biol. 52 25

    [69]

    Pershin Y V, Di Ventra M 2011 Adv. Phys. 60 145Google Scholar

    [70]

    Rumelhart D E, Hinton G E, Williams R J 1986 Nature 323 533Google Scholar

    [71]

    John H, Anders K, Palmer R G 1991 Phys. Today 44 70

    [72]

    Bi G Q, Poo M M 2001 Annu. Rev. Neurosci. 24 139Google Scholar

  • [1] Huang Yu-Hang, Chen Li-Xiang. Fractional Fourier Transform Imaging Based on Untrained Neural Networks. Acta Physica Sinica, 2024, 0(0): 0-0. doi: 10.7498/aps.73.20240050
    [2] Ma Rui-Yao, Wang Xin, Li Shu, Yong Heng, Shangguan Dan-Hua. An efficient calculation method for particle transport problems based on neural network. Acta Physica Sinica, 2024, 73(7): 072802. doi: 10.7498/aps.73.20231661
    [3] Chen Kai-Hui, Fan Zhen, Dong Shuai, Li Wen-Jie, Chen Yi-Hong, Tian Guo, Chen De-Yang, Qin Ming-Hui, Zeng Min, Lu Xu-Bing, Zhou Guo-Fu, Gao Xing-Sen, Liu Jun-Ming. Perovskite-phase interfacial intercalated layer-induced performance enhancement in SrFeOx-based memristors. Acta Physica Sinica, 2023, 72(9): 097301. doi: 10.7498/aps.72.20221934
    [4] Li Rui, Xu Bang-Lin, Zhou Jian-Fang, Jiang En-Hua, Wang Bing-Hong, Yuan Wu-Jie. A synaptic plasticity induced change in synaptic intensity variation and neurodynamic transition during awakening-sleep cycle. Acta Physica Sinica, 2023, 72(24): 248706. doi: 10.7498/aps.72.20231037
    [5] Fang Bo-Lang, Wang Jian-Guo, Feng Guo-Bin. Calculation of spot entroid based on physical informed neural networks. Acta Physica Sinica, 2022, 71(20): 200601. doi: 10.7498/aps.71.20220670
    [6] Li Jing, Sun Hao. Tag Z boson jets via convolutional neural networks. Acta Physica Sinica, 2021, 70(6): 061301. doi: 10.7498/aps.70.20201557
    [7] Zhu Wei, Liu Lan, Wen Chang-Bao, Li Jie. Spatiotemporal signal processing and device stability based on bi-layer biomimetic memristor. Acta Physica Sinica, 2021, 70(17): 178504. doi: 10.7498/aps.70.20210274
    [8] Sun Li-Wang, Li Hong, Wang Peng-Jun, Gao He-Bei, Luo Meng-Bo. Recognition of adsorption phase transition of polymer on surface by neural network. Acta Physica Sinica, 2019, 68(20): 200701. doi: 10.7498/aps.68.20190643
    [9] Gu Mei-Yuan, Liu Jing-Biao, Wang Guang-Yi, Liang Yan, Li Fu-Peng. Memcapacitor-based multivibrator and its experiments. Acta Physica Sinica, 2019, 68(22): 228401. doi: 10.7498/aps.68.20190849
    [10] Wei De-Zhi, Chen Fu-Ji, Zheng Xiao-Xue. Internet public opinion chaotic prediction based on chaos theory and the improved radial basis function in neural networks. Acta Physica Sinica, 2015, 64(11): 110503. doi: 10.7498/aps.64.110503
    [11] Li Huan, Wang You-Guo. Noise-enhanced information transmission of a non-linear multilevel threshold neural networks system. Acta Physica Sinica, 2014, 63(12): 120506. doi: 10.7498/aps.63.120506
    [12] Liu Yu-Dong, Wang Lian-Ming. Application of memristor-based spiking neural network in image edge extraction. Acta Physica Sinica, 2014, 63(8): 080503. doi: 10.7498/aps.63.080503
    [13] Chen Tie-Ming, Jiang Rong-Rong. New hybrid stream cipher based on chaos and neural networks. Acta Physica Sinica, 2013, 62(4): 040301. doi: 10.7498/aps.62.040301
    [14] Li Hua-Qing, Liao Xiao-Feng, Huang Hong-Yu. Synchronization of uncertain chaotic systems based on neural network and sliding mode control. Acta Physica Sinica, 2011, 60(2): 020512. doi: 10.7498/aps.60.020512
    [15] Zhao Hai-Quan, Zhang Jia-Shu. Adaptive nonlinear channel equalization based on combination neural network for chaos-based communication systems. Acta Physica Sinica, 2008, 57(7): 3996-4006. doi: 10.7498/aps.57.3996
    [16] Wang Yong-Sheng, Sun Jin, Wang Chang-Jin, Fan Hong-Da. Prediction of the chaotic time series from parameter-varying systems using artificial neural networks. Acta Physica Sinica, 2008, 57(10): 6120-6131. doi: 10.7498/aps.57.6120
    [17] Wang Rui-Min, Zhao Hong. The role of neuron transfer function in artificial neural networks. Acta Physica Sinica, 2007, 56(2): 730-739. doi: 10.7498/aps.56.730
    [18] Wang Yao-Nan, Tan Wen. Genetic-based neural network control for chaotic system. Acta Physica Sinica, 2003, 52(11): 2723-2728. doi: 10.7498/aps.52.2723
    [19] Tan Wen, Wang Yao-Nan, Liu Zhu-Run, Zhou Shao-Wu. . Acta Physica Sinica, 2002, 51(11): 2463-2466. doi: 10.7498/aps.51.2463
    [20] CHEN SHU, CHANG SHENG-JIANG, YUAN JING-HE, ZHANG YAN-XIN, K.W.WONG. ADAPTIVE TRAINING AND PRUNING FOR NEURAL NETWORKS:ALGORITHMS AND APPLICATION. Acta Physica Sinica, 2001, 50(4): 674-681. doi: 10.7498/aps.50.674
Metrics
  • Abstract views:  9093
  • PDF Downloads:  453
  • Cited By: 0
Publishing process
  • Received Date:  02 October 2020
  • Accepted Date:  06 November 2020
  • Available Online:  30 March 2021
  • Published Online:  05 April 2021

/

返回文章
返回