搜索

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

神经形态阻变器件在图像处理中的应用

江碧怡 周菲迟 柴扬

引用本文:
Citation:

神经形态阻变器件在图像处理中的应用

江碧怡, 周菲迟, 柴扬

Application of neuromorphic resistive random access memory in image processing

Jiang Bi-Yi, Zhou Fei-Chi, Chai Yang
PDF
HTML
导出引用
  • 随着搭载于边缘终端上的图像与视频等数据密集型应用的日益增长, 基于传统冯·诺依曼架构的互补金属氧化物半导体(complementary metal oxide semiconductor, CMOS)硬件系统正面临着能耗、速度和尺寸等多方面的挑战. 神经形态器件包括具有存算一体特性的电学阻变器件和具有感存算一体特性的光电阻变器件, 因其具有与生物神经系统的高相似度, 及其高能效、高集成度、宽带宽等优势, 在图像处理应用方面展现出巨大发展潜力. 这类器件不仅能够用于加速传统图像低阶预处理和高阶处理中的大量运算, 且能用于实现仿生物视觉系统的高效图像处理算法. 本文介绍了最近的电学及光电神经形态阻变器件, 并结合图像处理算法综述了神经形态阻变器件在图像处理方面的硬件实施和挑战, 并对其发展前景提出了思考.
    With the increasing demands for processing images and videos at edge terminals, complementary metal oxide semiconductor (CMOS) hardware systems based on conventional Von Neumann architectures are facing challenges in terms of energy consumption, speed, and footprint. Neuromorphic devices, including resistive random access memory with integrated storage-computation characteristic and optoelectronic resistive random access memory with highly integrated in-sensor computing characteristic, show great potential applications in image processing due to their high similarity to biological neural systems and advantages of high energy efficiency, high integration level, and wide bandwidth. These devices can be used not only to accelerate large numbers of computational tasks in conventional image preprocessing and higher-level image processing algorithms, but also to implement highly efficient biomimetic image processing algorithms. In this paper, we first introduce the state-of-the-art neuromorphic resistive random access memory and optoelectronic neuromorphic resistive random access memory, then review the hardware implementation of and challenges to image processing based on these devices, and finally provide perspectives of their future developments.
      通信作者: 周菲迟, zhoufc@sustech.edu.cn ; 柴扬, ychai@polyu.edu.cn
    • 基金项目: 国家自然科学基金(批准号: 62104091, 62174074)、广东省自然科学基金(批准号: 2022A1515011064)、广东省青年创新人才基金(批准号: 2021KQNCX077)和深圳市南山5G前沿项目(批准号: K21799131, K21799128)资助的课题.
      Corresponding author: Zhou Fei-Chi, zhoufc@sustech.edu.cn ; Chai Yang, ychai@polyu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 62104091, 62174074), the Natural Science Foundation of Guangdong Province, China (Grant No. 2022A1515011064), the Guangdong Youth Innovation Talent Fund, China (Grant No. 2021KQNCX077), and NSQKJJ Fund, China (Grant Nos. K21799131, K21799128).
    [1]

    Ma Y, Wu J, Long C, Lin Y B 2021 IEEE Internet Things J. 9 2802Google Scholar

    [2]

    Machida F, Andrade E 2021 2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC) Melbourne, Australia, May 10–13, 2021 p66

    [3]

    Pilli S K, Nallathambi B, George S J, Diwanji V 2015 2014 2nd International Conference on Electronics and Communication Systems (ICECS) Coimbatore, India, Feburary 26–27, 2014 p1

    [4]

    Chaki J, Dey N 2018 A Beginner's Guide to Image Preprocessing Techniques (Vol. 1) (Boca Raton: CRC Press)

    [5]

    Zhang J F, Lee C E, Liu C, Shao Y S, Keckler S W, Zhang Z 2019 2019 Symposium on VLSI Circuits Kyoto, Japan, June 9–14, 2019 pC306

    [6]

    Kinget P, Steyaert M S J 1995 IEEE J. Solid-State Circuits 30 235Google Scholar

    [7]

    Yin S, Ouyang P, Zheng S, Song D, Li X, Liu L, Wei S 2018 2018 IEEE Symposium on VLSI Circuits Honolulu, HI, USA, June 18–22, 2018 p139

    [8]

    Rao M V G, Kumar P R, Prasad A M 2016 2016 International Conference on Microelectronics, Computing and Communications (MicroCom) Durgapur, India, January 23–25, 2016 p1

    [9]

    Treichler D 1967 Film and AV Communication 1 14

    [10]

    Róka A, Csapó Á, Reskó B, Baranyi P 2007 Acta Polytech. Hung. 4 31

    [11]

    Wang W, Covi E, Milozzi A, Farronato M, Ricci S, Sbandati C, Pedretti G, Ielmini D 2021 Adv. Intell. Syst. 3 2000224Google Scholar

    [12]

    Webster M A 1996 Netw. Comput. Neural Syst. 7 587Google Scholar

    [13]

    Sabesan R, Schmidt Brian P, Tuten William S, Roorda A 2016 Sci. Adv. 2 e1600797Google Scholar

    [14]

    Cheng Z, Ríos C, Pernice W H P, Wright C D, Bhaskaran H 2017 Sci. Adv. 3 e1700160Google Scholar

    [15]

    Zhu X, Lu W D 2018 ACS Nano 12 1242Google Scholar

    [16]

    Liao F, Zhou F, Chai Y 2021 J. Semicond. 42 013105Google Scholar

    [17]

    Chai Y 2020 Nature 579 32Google Scholar

    [18]

    Li C, Guo J, Porikli F, Pang Y 2018 Pattern Recognit. Lett. 104 15Google Scholar

    [19]

    Khan M Z, Harous S, Hassan S U, Ghani Khan M U, Iqbal R, Mumtaz S 2019 IEEE Access 7 72622Google Scholar

    [20]

    Ni L, Huang H, Liu Z, Joshi R V, Yu H 2017 ACM J. Emerg. Technol. Comput. Syst. 13 1

    [21]

    Rajendran B, Alibart F 2016 IEEE J. Emerg. Sel. Top. Circuits Syst. 6 198Google Scholar

    [22]

    Shi T, Wang R, Wu Z, Sun Y, An J, Liu Q 2021 Small Struct. 2 2000109Google Scholar

    [23]

    Yang Y, Gao P, Gaba S, Chang T, Pan X, Lu W 2012 Nat. Commun. 3 732Google Scholar

    [24]

    Yuan F, Zhang Z, Liu C, Zhou F, Yau H M, Lu W, Qiu X, Wong H S P, Dai J, Chai Y 2017 ACS Nano 11 4097Google Scholar

    [25]

    Choi S, Tan S H, Li Z, Kim Y, Choi C, Chen P Y, Yeon H, Yu S, Kim J 2018 Nat. Mater. 17 335Google Scholar

    [26]

    Chandrasekaran S, Simanjuntak F M, Saminathan R, Panda D, Tseng T Y 2019 Nanotechnology 30 445205Google Scholar

    [27]

    Zhao X, Zhang K, Hu K, Zhang Y, Zhou Q, Wang Z, She Y, Zhang Z, Wang F 2021 IEEE Trans. Electron Devices 68 6100Google Scholar

    [28]

    Dash C S, Sahoo S, Prabaharan S R S 2018 Solid State Ionics 324 218Google Scholar

    [29]

    Nili H, Ahmed T, Walia S, Ramanathan R, Kandjani A E, Rubanov S, Kim J, Kavehei O, Bansal V, Bhaskaran M, Sriram S 2016 Nanotechnology 27 505210Google Scholar

    [30]

    Simanjuntak F M, Chandrasekaran S, Lin C C, Tseng T Y 2019 APL Mater. 7 051108Google Scholar

    [31]

    Chen J Y, Hsin C L, Huang C W, Chiu C H, Huang Y T, Lin S J, Wu W W, Chen L J 2013 Nano Lett. 13 3671Google Scholar

    [32]

    Wu W, Wu H, Gao B, Deng N, Yu S, Qian H 2017 IEEE Electron Device Lett. 38 1019Google Scholar

    [33]

    Park E, Kim M, Kim T S, Kim I S, Park J, Kim J, Jeong Y, Lee S, Kim I, Park J K, Kim G T, Chang J, Kang K, Kwak J Y 2020 Nanoscale 12 24503Google Scholar

    [34]

    Seo S, Kang B S, Lee J J, Ryu H J, Kim S, Kim H, Oh S, Shim J, Heo K, Oh S, Park J H 2020 Nat. Commun. 11 3936Google Scholar

    [35]

    Yang C S, Shang D S, Liu N, Fuller E J, Agrawal S, Talin A A, Li Y Q, Shen B G, Sun Y 2018 Adv. Funct. Mater. 28 1804170Google Scholar

    [36]

    Zhang W, Pan L, Yan X, Zhao G, Chen H, Wang X, Tay B K, Zhong G, Li J, Huang M 2021 Adv. Intell. Syst. 3 2100041Google Scholar

    [37]

    Bayat F M, Prezioso M, Chakrabarti B, Nili H, Kataeva I, Strukov D 2018 Nat. Commun. 9 2331Google Scholar

    [38]

    Sheridan P M, Cai F, Du C, Ma W, Zhang Z, Lu W D 2017 Nat. Nanotechnol. 12 784Google Scholar

    [39]

    Cassuto Y, Kvatinsky S, Yaakobi E 2013 2013 IEEE International Symposium on Information Theory Istanbul, Turkey, July 7–12, 2013 p156

    [40]

    Yao P, Wu H, Gao B, Eryilmaz S B, Huang X, Zhang W, Zhang Q, Deng N, Shi L, Wong H P, Qian H 2017 Nat. Commun. 8 15199Google Scholar

    [41]

    Li C, Wang Z, Rao M, Belkin D, Song W, Jiang H, Yan P, Li Y, Lin P, Hu M, Ge N, Strachan J P, Barnell M, Wu Q, Williams R S, Yang J J, Xia Q 2019 Nat. Mach. Intell. 1 49Google Scholar

    [42]

    Li Y, Tang J, Gao B, Sun W, Hua Q, Zhang W, Li X, Zhang W, Qian H, Wu H 2020 Adv. Sci. 7 2002251Google Scholar

    [43]

    Zhao X, Ma J, Xiao X, Liu Q, Shao L, Chen D, Liu S, Niu J, Zhang X, Wang Y, Cao R, Wang W, Di Z, Lv H, Long S, Liu M 2018 Adv. Mater. 30 1705193Google Scholar

    [44]

    Choi B J, Zhang J, Norris K, Gibson G, Kim K M, Jackson W, Zhang M X, Li Z, Yang J J, Williams R S 2016 Adv. Mater. 28 356Google Scholar

    [45]

    Ohba K, Yasuda S, Mizuguchi T, Sei H, Tsushima T, Shimuta M, Shiimoto T, Yamamoto T, Sone T, Nonoguchi S, Kouchiyama A, Otsuka W, Aratani K, Tsutsui K 2018 2018 IEEE International Memory Workshop (IMW) Kyoto, Japan, May 13–16, 2018 p1

    [46]

    Kim W G, Lee H M, Kim B Y, Jung K H, Seong T G, Kim S, Jung H C, Kim H J, Yoo J H, Lee H D, Kim S G 2014 2014 Symposium on VLSI Technology (VLSI-Technology): Digest of Technical Papers Honolulu, HI, USA, June 9–12, 2014 p1

    [47]

    Lu D, Zhao Y, Anh T X, Yu Y H, Huang D, Lin Y, Ding S J, Wang P F, Li M F 2014 IEEE Trans. Electron Devices 61 2294Google Scholar

    [48]

    Farsa E Z, Ahmadi A, Maleki M A, Gholami M, Rad H N 2019 IEEE Trans. Circuits Syst. II Express Briefs 66 1582Google Scholar

    [49]

    Hu D, Zhang X, Xu Z, Ferrari S, Mazumder P 2014 14th IEEE International Conference on Nanotechnology Toronto, Canada, August 18–21, 2014 p873

    [50]

    Lameu E L, Borges F S, Iarosz K C, Protachevicz P R, Antonopoulos C G, Macau E E N, Batista A M 2021 Commun. Nonlinear Sci. Numer. Simul. 96 105689Google Scholar

    [51]

    Tsodyks M V, Markram H 1997 Proc. Natl. Acad. Sci. USA 94 719Google Scholar

    [52]

    Meftah B, Lezoray O, Benyettou A 2010 Neural Process. Lett. 32 131Google Scholar

    [53]

    Iakymchuk T, Rosado Muñoz A, Guerrero Martínez J F, Bataller Mompeán M, Francés Víllora J V 2015 Eurasip J. Image Video Process. 2015 4Google Scholar

    [54]

    Cho S G, Beigne E, Zhang Z 2019 2019 IEEE Custom Integrated Circuits Conference (CICC) Austin, TX, USA, April 14–17, 2019 p1

    [55]

    Yan X, Zhao J, Liu S, Zhou Z, Liu Q, Chen J, Liu X Y 2018 Adv. Funct. Mater. 28 1705320Google Scholar

    [56]

    Yan X, Qin C, Lu C, Zhao J, Zhao R, Ren D, Zhou Z, Wang H, Wang J, Zhang L, Li X, Pei Y, Wang G, Zhao Q, Wang K, Xiao Z, Li H 2019 ACS Appl. Mater. Interfaces 11 48029Google Scholar

    [57]

    Yan X, Wang K, Zhao J, Zhou Z, Wang H, Wang J, Zhang L, Li X, Xiao Z, Zhao Q, Pei Y, Wang G, Qin C, Li H, Lou J, Liu Q, Zhou P 2019 Small 15 1900107Google Scholar

    [58]

    Lee T H, Hwang H G, Woo J U, Kim D H, Kim T W, Nahm S 2018 ACS Appl. Mater. Interfaces 10 25673Google Scholar

    [59]

    Wang Z, Joshi S, Savel'ev S E, Jiang H, Midya R, Lin P, Hu M, Ge N, Strachan J P, Li Z, Wu Q, Barnell M, Li G L, Xin H L, Williams R S, Xia Q, Yang J J 2017 Nat. Mater. 16 101Google Scholar

    [60]

    Yang J T, Ge C, Du J Y, Huang H Y, He M, Wang C, Lu H B, Yang G Z, Jin K J 2018 Adv. Mater. 30 1801548Google Scholar

    [61]

    Li Y, Lu J, Shang D, Liu Q, Wu S, Wu Z, Zhang X, Yang J, Wang Z, Lv H, Liu M 2020 Adv. Mater. 32 2003018Google Scholar

    [62]

    Mukherjee A, Sagar S, Parveen S, Das B C 2021 Appl. Phys. Lett. 119 253502Google Scholar

    [63]

    Liang F X, Wang I T, Hou T H 2021 Adv. Intell. Syst. 3 2100007Google Scholar

    [64]

    Zhang X, Wang W, Liu Q, Zhao X, Wei J, Cao R, Yao Z, Zhu X, Zhang F, Lv H, Long S, Liu M 2018 IEEE Electron Device Lett. 39 308Google Scholar

    [65]

    Duan Q, Jing Z, Zou X, Wang Y, Yang K, Zhang T, Wu S, Huang R, Yang Y 2020 Nat. Commun. 11 3399Google Scholar

    [66]

    Lu Y F, Li Y, Li H, Wan T Q, Huang X, He Y H, Miao X 2020 IEEE Electron Device Lett. 41 1245Google Scholar

    [67]

    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

    [68]

    Wang Y, Chen X, Shen D, Zhang M, Chen X, Chen X, Shao W, Gu H, Xu J, Hu E, Wang L, Xu R, Tong Y 2021 Nanomaterials 11 2860Google Scholar

    [69]

    Bousoulas P, Panagopoulou M, Boukos N, Tsoukalas D 2021 J. Phys. D:Appl. Phys. 54 225303Google Scholar

    [70]

    Han J K, Oh J, Yun G J, Yoo D, Kim M S, Yu J M, Choi S Y, Choi Y K 2021 Sci. Adv. 7 eabg8836Google Scholar

    [71]

    Wan T, Ma S, Liao F, Fan L, Chai Y 2022 Sci. China Inf. Sci. 65 141401Google Scholar

    [72]

    Wang T Y, Meng J L, Li Q X, He Z Y, Zhu H, Ji L, Sun Q Q, Chen L, Zhang D W 2021 Nano Energy 89 106291Google Scholar

    [73]

    Meng J, Wang T, Zhu H, Ji L, Bao W, Zhou P, Chen L, Sun Q Q, Zhang D W 2022 Nano Lett. 22 81Google Scholar

    [74]

    Seo S, Jo S H, Kim S, Shim J, Oh S, Kim J H, Heo K, Choi J W, Choi C, Oh S, Kuzum D, Wong H P, Park J H 2018 Nat. Commun. 9 5106Google Scholar

    [75]

    Liao F, Zhou Z, Kim B J, Chen J, Wang J, Wan T, Zhou Y, Hoang A T, Wang C, Kang J, Ahn J H, Chai Y 2022 Nat. Electron. 5 84Google Scholar

    [76]

    Gao S, Liu G, Yang H, Hu C, Chen Q, Gong G, Xue W, Yi X, Shang J, Li R W 2019 ACS Nano 13 2634Google Scholar

    [77]

    Tan H, Tao Q, Pande I, Majumdar S, Liu F, Zhou Y, Persson P O A, Rosen J, van Dijken S 2020 Nat. Commun. 11 1369Google Scholar

    [78]

    Zhou F, Zhou Z, Chen J, Choy T H, Wang J, Zhang N, Lin Z, Yu S, Kang J, Wong H S P, Chai Y 2019 Nat. Nanotechnol. 14 776Google Scholar

    [79]

    Liu L, Cheng Z, Jiang B, Liu Y, Zhang Y, Yang F, Wang J, Yu X F, Chu P K, Ye C 2021 ACS Appl. Mater. Interfaces 13 30797Google Scholar

    [80]

    Zhou F, Chen J, Tao X, Wang X, Chai Y 2019 Research 2019 9490413

    [81]

    Xiang D, Liu T, Xu J, Tan J Y, Hu Z, Lei B, Zheng Y, Wu J, Neto A H C, Liu L, Chen W 2018 Nat. Commun. 9 2966Google Scholar

    [82]

    Zhang Z, Wang S, Liu C, Xie R, Hu W, Zhou P 2022 Nat. Nanotechnol. 17 27Google Scholar

    [83]

    Wang S, Chen C, Yu Z, He Y, Chen X, Wan Q, Shi Y, Zhang D W, Zhou H, Wang X, Zhou P 2019 Adv. Mater. 31 1806227Google Scholar

    [84]

    Zhu Q B, Li B, Yang D D, Liu C, Feng S, Chen M L, Sun Y, Tian Y N, Su X, Wang X M, Qiu S, Li Q W, Li X M, Zeng H B, Cheng H M, Sun D M 2021 Nat. Commun. 12 1798Google Scholar

    [85]

    Hu L, Yang J, Wang J, Cheng P, Chua L O, Zhuge F 2021 Adv. Funct. Mater. 31 2005582Google Scholar

    [86]

    Hou Y X, Li Y, Zhang Z C, Li J Q, Qi D H, Chen X D, Wang J J, Yao B W, Yu M X, Lu T B, Zhang J 2021 ACS Nano 15 1497Google Scholar

    [87]

    Yang L, Singh M, Shen S W, Chih K Y, Liu S W, Wu C I, Chu C W, Lin H W 2020 Adv. Funct. Mater. 31 2008259Google Scholar

    [88]

    Pei Y, Yan L, Wu Z, Lu J, Zhao J, Chen J, Liu Q, Yan X 2021 ACS Nano 15 17319Google Scholar

    [89]

    John R A, Acharya J, Zhu C, Surendran A, Bose S K, Chaturvedi A, Tiwari N, Gao Y, He Y, Zhang K K, Xu M, Leong W L, Liu Z, Basu A, Mathews N 2020 Nat. Commun. 11 3211Google Scholar

    [90]

    Egmont Petersen M, De Ridder D, Handels H 2002 Pattern Recognit. 35 2279Google Scholar

    [91]

    Rao D H, Panduranga P P 2006 2006 IEEE International Conference on Industrial Technology Mumbai, India, December 15–17, 2006 p2821

    [92]

    Chakraborty D, Raj S, Fernandes S L, Jha S K 2019 IEEE J. Emerging Sel. Top. Circuits Syst. 9 580Google Scholar

    [93]

    Pannu J S, Raj S, Fernandes S L, Chakraborty D, Rafiq S, Cady N, Jha S K 2020 IEEE Trans. Circuits Syst. II Express Briefs 67 961Google Scholar

    [94]

    Mannion D J, Mehonic A, Ng W H, Kenyon A J 2019 Front. Neurosci. 13 1386Google Scholar

    [95]

    Li C, Hu M, Li Y, Jiang H, Ge N, Montgomery E, Zhang J, Song W, Dávila N, Graves C E, Li Z, Strachan J P, Lin P, Wang Z, Barnell M, Wu Q, Williams R S, Yang J J, Xia Q 2017 Nat. Electron. 1 52

    [96]

    Lin P, Li C, Wang Z, Li Y, Jiang H, Song W, Rao M, Zhuo Y, Upadhyay N K, Barnell M, Wu Q, Yang J J, Xia Q 2020 Nat. Electron. 3 225Google Scholar

    [97]

    Pajouhi Z, Roy K 2018 IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37 1774Google Scholar

    [98]

    Yu Y, Deng Q, Ren L, Tashi N 2020 Neural Process. Lett. 51 1891Google Scholar

    [99]

    Bao L, Kang J, Fang Y, Yu Z, Wang Z, Yang Y, Cai Y, Huang R 2018 Sci. Rep. 8 13727Google Scholar

    [100]

    Hu W C, Yang C Y, Huang D Y 2011 J. Visual Commun. Image Represent. 22 543Google Scholar

    [101]

    Somasundaram G, Sivalingam R, Morellas V, Papanikolopoulos N 2013 IEEE Trans. Intell. Transp. Syst. 14 69Google Scholar

    [102]

    Huang K, Wang L, Tan T, Maybank S 2008 Pattern Recognit. 41 432Google Scholar

    [103]

    Maan A K, Kumar D S, Sugathan S, James A P 2015 IEEE Trans. Very Large Scale Integr. VLSI Syst. 23 2337Google Scholar

    [104]

    Jayachandran D, Oberoi A, Sebastian A, Choudhury T H, Shankar B, Redwing J M, Das S 2020 Nat. Electron. 3 646Google Scholar

    [105]

    Wang Y, Gong Y, Huang S, Xing X, Lv Z, Wang J, Yang J Q, Zhang G, Zhou Y, Han S T 2021 Nat. Commun. 12 5979Google Scholar

    [106]

    Russo F 2002 IEEE Trans. Instrum. Meas. 51 824Google Scholar

    [107]

    Wang C Y, Liang S J, Wang S, Wang P, Li Z a, Wang Z, Gao A, Pan C, Liu C, Liu J, Yang H, Liu X, Song W, Wang C, Cheng B, Wang X, Chen K, Wang Z, Watanabe K, Taniguchi T, Yang J J, Miao F 2020 Sci. Adv. 6 eaba6173Google Scholar

    [108]

    Yang X, Xiong Z, Chen Y, Ren Y, Zhou L, Li H, Zhou Y, Pan F, Han S T 2020 Nano Energy 78 105246Google Scholar

    [109]

    Zhu R, Tang Z, Ye S, Huang Q, Guo L, Chang S 2021 IEEE Trans. Electron Devices 68 602Google Scholar

    [110]

    Tang Z, Zhu R, Hu R, Chen Y, Wu E Q, Wang H, He J, Huang Q, Chang S 2021 IEEE Trans. Cognit. Dev. Syst. 13 645Google Scholar

    [111]

    Xin M, Wang Y 2019 Eurasip J. Image Video Process. 2019 40Google Scholar

    [112]

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

    [113]

    Bouvier M, Valentian A, Mesquida T, Rummens F, Reyboz M, Vianello E, Beigne E 2019 ACM J. Emerging Technol. Comput. Syst. 15 1

    [114]

    Boybat I, Le Gallo M, Nandakumar S R, Moraitis T, Parnell T, Tuma T, Rajendran B, Leblebici Y, Sebastian A, Eleftheriou E 2018 Nat. Commun. 9 2514Google Scholar

    [115]

    Wang Z, Joshi S, Savel’Ev S, Song W, Midya R, Li Y, Rao M, Yan P, Asapu S, Zhuo Y, Jiang H, Lin P, Li C, Yoon J H, Upadhyay N K, Zhang J, Hu M, Strachan J P, Barnell M, Wu Q, Wu H, Williams R S, Xia Q, Yang J J 2018 Nat. Electron. 1 137Google Scholar

    [116]

    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

  • 图 1  氧空位导电丝型RRAM[31] (a) Pt/ZnO/Pt RRAM内生成的氧空位导电丝; (b) Pt/ZnO/Pt RRAM内导电丝的断裂; (c) 导电丝生成(蓝)/断裂(红)过程中器件的I-V特性曲线

    Fig. 1.  Oxygen vacancy conductive filament in RRAM[31]: (a) Oxygen vacancy conductive filament formed in Pt/ZnO/Pt RRAM; (b) rupture of conductive filament in Pt/ZnO/Pt RRAM; (c) I-V characteristic curves of the device during conductive filament formation (blue) and rupture (red).

    图 2  应用于ANN的神经形态RRAM阵列 (a) 1R阵列的VMM运算示意图[36]; (b) 1T1R阵列实现ANN的方式[40]; (c) 1S1R阵列结构[42]

    Fig. 2.  Neuromorphic RRAM arrays applied to ANN: (a) Schematic diagram of the VMM operation of 1R array[36]; (b) method of implementing ANN with 1T1R array[40]; (c) structure of 1S1R array[42].

    图 3  Pt/KNbO3/TiN 神经形态RRAM[58] (a) 具有40 μm时间差的突触前脉冲(红)和突触后脉冲(绿), 以及对应的等效输入脉冲(蓝); (b) 器件的STDP特性; (c) STP($ {I}_{2}-{I}_{1} $)和PTP($ {I}_{10}-{I}_{1} $)特性

    Fig. 3.  Pt/KNbO3/TiN neuromorphic RRAM[58]: (a) Presynaptic pulse (red) and postsynaptic pulse (green) with 40 μm time difference, and the equivalent input pulse (blue) of the RRAM; (b) STDP characteristic; (c) STP ($ {I}_{2}-{I}_{1} $) and PTP ($ {I}_{10}-{I}_{1} $) characteristics.

    图 4  两端Ag/V2C/W型RRAM器件[68] (a) RRAM的TS特性; (b) RRAM作为人工LIF神经元(左)和神经元的LIF行为(右); LIF人工神经元输出脉冲频率受(c)输入脉冲频率和(d) 输入脉冲幅值调控

    Fig. 4.  Two-terminal Ag/V2C/W type RRAM[68]: (a) TS characteristic of RRAM; (b) RRAM as an artificial LIF neuron (left) and the corresponding LIF behavior (right); modulation of LIF artificial neuron output frequency by (c) the input pulse frequency and (d) the input pulse amplitude.

    图 5  Si/SiO2/Si3N4/SiO2/Si浮栅型神经形态RRAM[70] (a) 作为人工神经元; (b) 作为人工突触; (c) 人工突触和突触后神经元连接方式; (d) 人工神经元LIF行为产生的输出脉冲频率与所连接的人工突触权重大小的关系

    Fig. 5.  Si/SiO2/Si3N4/SiO2/Si floating gate neuromorphic RRAM device[70]: (a) As artificial neuron; (b) as artificial synapse; (c) connection of artificial synapse and postsynaptic artificial neuron; (d) effects of connected synaptic weight on the artificial LIF neuron output frequency.

    图 6  ITO/Nb-SrTiO3/Ag结构的神经形态ORRAM[76] (a) 光电调控的阻变机理; (b) 通过改变输入光脉冲频率或数量实现的STP和LTP特性之间的转换; (c) 器件阵列记忆强度随输入电压幅值增加而增强的特性

    Fig. 6.  ITO/Nb-SrTiO3/Ag neuromorphic ORRAM[76]: (a) Optoelectronic resistive switching mechanism; (b) transition between STP and LTP characteristics by changing the frequency or number of input optical pulses; (c) enhanced memory characteristics in the array with increased input voltage amplitude.

    图 7  ITO/MoOx/Pd 神经形态ORRAM[78] (a) 器件结构; (b) 基于Mo离子价态转变的电阻调控机理; (c) ORRAM的STP特性; (d) ORRAM的LTP特性

    Fig. 7.  ITO/MoOx/Pd neuromorphic ORRAM[78]: (a) Device structure; (b) resistive switching mechanism based on change of Mo ion valence state; (c) STP characteristic of ORRAM; (d) LTP characteristic of ORRAM.

    图 8  基于BN/WSe2异质结结构的三端ORRAM[81] (a) 器件结构; (b) 光电调控的阻变原理; (c) ORRAM组成的阵列对不同波长光输入的不同存储效应

    Fig. 8.  Three-terminal ORRAM device based on BN/WSe2 heterostructure[81]: (a) Device structure; (b) switching mechanisms; (c) different storage levels resulted from different light wavelengths in ORRAM array.

    图 9  Au/富氧IGZO/缺氧IGZO/Pt结构的ORRAM[85] (a) 器件结构; (b) 可见光脉冲(420 nm)使器件电导率上升和近红外光脉冲(800 nm)使器件电导率降低的过程; (c) 光调控的STDP特性

    Fig. 9.  Au/oxygen-deficient IGZO/oxygen-rich IGZO/Pt ORRAM[85]: (a) Device structure; (b) conductivity increasing realized by visible light pulses (420 nm) and conductivity decreasing realized by near-infrared light pulses (800 nm); (c) light modulated STDP characteristic.

    图 10  基于ReS2 ORRAM与CMOS LIF神经元构建的光可调控神经元[89] (a) 光可调控神经元结构; (b) 光可调控神经元输出脉冲频率在光照下增加的行为

    Fig. 10.  Light tunable artificial neuron based on ReS2 ORRAM and CMOS LIF neuron [89]: (a) Structure of light tunable artificial neuron; (b) increasing of light tunable artificial neuron output frequency in response to light illumination.

    图 11  基于神经形态阻变器件频率差检测电路实现的图像边缘提取[94] (a) 基于RRAM分压器的频率差检测电路(右)和所使用的器件结构(左); (b) 两组输入脉冲频率相同(左)和不同(右)时频率差检测电路的输出; (c) 原图和频率差检测电路提取的图片边缘

    Fig. 11.  Edge detection based on frequency difference circuit implemented by neuromorphic RRAM[94]: (a) Frequency difference detection circuit (right) and the adopted RRAM (left); (b) output of the frequency difference detection circuit when two sets of the input pulses are at the same frequency (left) and different frequencies (right), respectively; (c) original image and extracted edges by frequency difference detection circuit.

    图 12  基于RRAM和CMOS晶体管人工视网膜单元实现的边缘提取[99] (a) 生物视网膜系统(光感受-双极-神经节细胞)对不同输入光照的不同输出脉冲频率; (b) 人工视网膜单元结构; (c) 人工视网膜单元输出信号V0Vth端口输入信号和input端口输入信号的变化

    Fig. 12.  Unit of artificial retinal system based on RRAM for edge extraction[99]: (a) Different output frequencies of the biological retinal system (photoreceptor cells-bipolar cells-ganglion cells) in response to different light pulse inputs; (b) structure of artificial retinal system unit; (c) change of the artificial retinal system unit output signal V0 with respect to input signals from Vth port and input port

    图 13  基于Ag/HfO2/C RRAM人工神经节细胞实现的运动检测[11] (a) 具有给光/撤光反应机制的生物视网膜系统结构; (b) 人工神经节细胞结构; (c) 人工神经节细胞工作原理; (d) 包含4个人工神经节细胞的RRAM阵列

    Fig. 13.  Artificial ganglion cell based on Ag/HfO2/C RRAM for motion detection[11]: (a) Structure of biological retinal system with both excitation and inhibition response to optical input; (b) structure of artificial ganglion cells; (c) working principle of artificial ganglion cells; (d) RRAM array realized with four artificial ganglion cells.

    图 14  基于Ag/FLBP-CsPbBr3/ITO ORRAM类眼球形阵列实现的运动检测[105] (a) 单个器件的结构; (b) 生物LGMD细胞输出脉冲频率对接近物体的非线性反应; (c) 基于Ag/FLBP-CsPbBr3/ITO ORRAM实现的人工LGMD神经元对生物LGMD神经元非线性响应特性的模仿; (d) 柔性Ag/FLBP-CsPbBr3/ITO ORRAM构建的类眼球形阵列

    Fig. 14.  Ag/FLBP-CsPbBr3/ITO ORRAM array based biometric compound eye for motion detection[105]: (a) Structure of single device; (b) nonlinear response to approaching objects regarding output spike frequency of biological LGMD cell; (c) emulation of the nonlinear response properties in biological LGMD neuron by artificial LGMD neuron based on Ag/FLBP-CsPbBr3/ITO ORRAM; (d) flexible Ag/FLBP-CsPbBr3/ITO ORRAM array as biometric compound eye.

    图 15  基于ITO/MoOx/Pd ORRAM阵列实现的图像锐化[78] (a) 8$ \times $8 ITO/MoOx/Pd ORRAM阵列; (b) ORRAM阵列的非线性阻变特性; (c) 基于ORRAM图像锐化阵列和图像识别神经网络的人工视觉系统

    Fig. 15.  ITO/MoOx/Pd ORRAM array for image sharpening[78]: (a) 8$ \times $8 ITO/MoOx/Pd ORRAM array; (b) nonlinear resistance switching characteristics of the ORRAM array; (c) an artificial vision system based on ORRAM image sharpening array and image recognition neural network.

  • [1]

    Ma Y, Wu J, Long C, Lin Y B 2021 IEEE Internet Things J. 9 2802Google Scholar

    [2]

    Machida F, Andrade E 2021 2021 IEEE 5th International Conference on Fog and Edge Computing (ICFEC) Melbourne, Australia, May 10–13, 2021 p66

    [3]

    Pilli S K, Nallathambi B, George S J, Diwanji V 2015 2014 2nd International Conference on Electronics and Communication Systems (ICECS) Coimbatore, India, Feburary 26–27, 2014 p1

    [4]

    Chaki J, Dey N 2018 A Beginner's Guide to Image Preprocessing Techniques (Vol. 1) (Boca Raton: CRC Press)

    [5]

    Zhang J F, Lee C E, Liu C, Shao Y S, Keckler S W, Zhang Z 2019 2019 Symposium on VLSI Circuits Kyoto, Japan, June 9–14, 2019 pC306

    [6]

    Kinget P, Steyaert M S J 1995 IEEE J. Solid-State Circuits 30 235Google Scholar

    [7]

    Yin S, Ouyang P, Zheng S, Song D, Li X, Liu L, Wei S 2018 2018 IEEE Symposium on VLSI Circuits Honolulu, HI, USA, June 18–22, 2018 p139

    [8]

    Rao M V G, Kumar P R, Prasad A M 2016 2016 International Conference on Microelectronics, Computing and Communications (MicroCom) Durgapur, India, January 23–25, 2016 p1

    [9]

    Treichler D 1967 Film and AV Communication 1 14

    [10]

    Róka A, Csapó Á, Reskó B, Baranyi P 2007 Acta Polytech. Hung. 4 31

    [11]

    Wang W, Covi E, Milozzi A, Farronato M, Ricci S, Sbandati C, Pedretti G, Ielmini D 2021 Adv. Intell. Syst. 3 2000224Google Scholar

    [12]

    Webster M A 1996 Netw. Comput. Neural Syst. 7 587Google Scholar

    [13]

    Sabesan R, Schmidt Brian P, Tuten William S, Roorda A 2016 Sci. Adv. 2 e1600797Google Scholar

    [14]

    Cheng Z, Ríos C, Pernice W H P, Wright C D, Bhaskaran H 2017 Sci. Adv. 3 e1700160Google Scholar

    [15]

    Zhu X, Lu W D 2018 ACS Nano 12 1242Google Scholar

    [16]

    Liao F, Zhou F, Chai Y 2021 J. Semicond. 42 013105Google Scholar

    [17]

    Chai Y 2020 Nature 579 32Google Scholar

    [18]

    Li C, Guo J, Porikli F, Pang Y 2018 Pattern Recognit. Lett. 104 15Google Scholar

    [19]

    Khan M Z, Harous S, Hassan S U, Ghani Khan M U, Iqbal R, Mumtaz S 2019 IEEE Access 7 72622Google Scholar

    [20]

    Ni L, Huang H, Liu Z, Joshi R V, Yu H 2017 ACM J. Emerg. Technol. Comput. Syst. 13 1

    [21]

    Rajendran B, Alibart F 2016 IEEE J. Emerg. Sel. Top. Circuits Syst. 6 198Google Scholar

    [22]

    Shi T, Wang R, Wu Z, Sun Y, An J, Liu Q 2021 Small Struct. 2 2000109Google Scholar

    [23]

    Yang Y, Gao P, Gaba S, Chang T, Pan X, Lu W 2012 Nat. Commun. 3 732Google Scholar

    [24]

    Yuan F, Zhang Z, Liu C, Zhou F, Yau H M, Lu W, Qiu X, Wong H S P, Dai J, Chai Y 2017 ACS Nano 11 4097Google Scholar

    [25]

    Choi S, Tan S H, Li Z, Kim Y, Choi C, Chen P Y, Yeon H, Yu S, Kim J 2018 Nat. Mater. 17 335Google Scholar

    [26]

    Chandrasekaran S, Simanjuntak F M, Saminathan R, Panda D, Tseng T Y 2019 Nanotechnology 30 445205Google Scholar

    [27]

    Zhao X, Zhang K, Hu K, Zhang Y, Zhou Q, Wang Z, She Y, Zhang Z, Wang F 2021 IEEE Trans. Electron Devices 68 6100Google Scholar

    [28]

    Dash C S, Sahoo S, Prabaharan S R S 2018 Solid State Ionics 324 218Google Scholar

    [29]

    Nili H, Ahmed T, Walia S, Ramanathan R, Kandjani A E, Rubanov S, Kim J, Kavehei O, Bansal V, Bhaskaran M, Sriram S 2016 Nanotechnology 27 505210Google Scholar

    [30]

    Simanjuntak F M, Chandrasekaran S, Lin C C, Tseng T Y 2019 APL Mater. 7 051108Google Scholar

    [31]

    Chen J Y, Hsin C L, Huang C W, Chiu C H, Huang Y T, Lin S J, Wu W W, Chen L J 2013 Nano Lett. 13 3671Google Scholar

    [32]

    Wu W, Wu H, Gao B, Deng N, Yu S, Qian H 2017 IEEE Electron Device Lett. 38 1019Google Scholar

    [33]

    Park E, Kim M, Kim T S, Kim I S, Park J, Kim J, Jeong Y, Lee S, Kim I, Park J K, Kim G T, Chang J, Kang K, Kwak J Y 2020 Nanoscale 12 24503Google Scholar

    [34]

    Seo S, Kang B S, Lee J J, Ryu H J, Kim S, Kim H, Oh S, Shim J, Heo K, Oh S, Park J H 2020 Nat. Commun. 11 3936Google Scholar

    [35]

    Yang C S, Shang D S, Liu N, Fuller E J, Agrawal S, Talin A A, Li Y Q, Shen B G, Sun Y 2018 Adv. Funct. Mater. 28 1804170Google Scholar

    [36]

    Zhang W, Pan L, Yan X, Zhao G, Chen H, Wang X, Tay B K, Zhong G, Li J, Huang M 2021 Adv. Intell. Syst. 3 2100041Google Scholar

    [37]

    Bayat F M, Prezioso M, Chakrabarti B, Nili H, Kataeva I, Strukov D 2018 Nat. Commun. 9 2331Google Scholar

    [38]

    Sheridan P M, Cai F, Du C, Ma W, Zhang Z, Lu W D 2017 Nat. Nanotechnol. 12 784Google Scholar

    [39]

    Cassuto Y, Kvatinsky S, Yaakobi E 2013 2013 IEEE International Symposium on Information Theory Istanbul, Turkey, July 7–12, 2013 p156

    [40]

    Yao P, Wu H, Gao B, Eryilmaz S B, Huang X, Zhang W, Zhang Q, Deng N, Shi L, Wong H P, Qian H 2017 Nat. Commun. 8 15199Google Scholar

    [41]

    Li C, Wang Z, Rao M, Belkin D, Song W, Jiang H, Yan P, Li Y, Lin P, Hu M, Ge N, Strachan J P, Barnell M, Wu Q, Williams R S, Yang J J, Xia Q 2019 Nat. Mach. Intell. 1 49Google Scholar

    [42]

    Li Y, Tang J, Gao B, Sun W, Hua Q, Zhang W, Li X, Zhang W, Qian H, Wu H 2020 Adv. Sci. 7 2002251Google Scholar

    [43]

    Zhao X, Ma J, Xiao X, Liu Q, Shao L, Chen D, Liu S, Niu J, Zhang X, Wang Y, Cao R, Wang W, Di Z, Lv H, Long S, Liu M 2018 Adv. Mater. 30 1705193Google Scholar

    [44]

    Choi B J, Zhang J, Norris K, Gibson G, Kim K M, Jackson W, Zhang M X, Li Z, Yang J J, Williams R S 2016 Adv. Mater. 28 356Google Scholar

    [45]

    Ohba K, Yasuda S, Mizuguchi T, Sei H, Tsushima T, Shimuta M, Shiimoto T, Yamamoto T, Sone T, Nonoguchi S, Kouchiyama A, Otsuka W, Aratani K, Tsutsui K 2018 2018 IEEE International Memory Workshop (IMW) Kyoto, Japan, May 13–16, 2018 p1

    [46]

    Kim W G, Lee H M, Kim B Y, Jung K H, Seong T G, Kim S, Jung H C, Kim H J, Yoo J H, Lee H D, Kim S G 2014 2014 Symposium on VLSI Technology (VLSI-Technology): Digest of Technical Papers Honolulu, HI, USA, June 9–12, 2014 p1

    [47]

    Lu D, Zhao Y, Anh T X, Yu Y H, Huang D, Lin Y, Ding S J, Wang P F, Li M F 2014 IEEE Trans. Electron Devices 61 2294Google Scholar

    [48]

    Farsa E Z, Ahmadi A, Maleki M A, Gholami M, Rad H N 2019 IEEE Trans. Circuits Syst. II Express Briefs 66 1582Google Scholar

    [49]

    Hu D, Zhang X, Xu Z, Ferrari S, Mazumder P 2014 14th IEEE International Conference on Nanotechnology Toronto, Canada, August 18–21, 2014 p873

    [50]

    Lameu E L, Borges F S, Iarosz K C, Protachevicz P R, Antonopoulos C G, Macau E E N, Batista A M 2021 Commun. Nonlinear Sci. Numer. Simul. 96 105689Google Scholar

    [51]

    Tsodyks M V, Markram H 1997 Proc. Natl. Acad. Sci. USA 94 719Google Scholar

    [52]

    Meftah B, Lezoray O, Benyettou A 2010 Neural Process. Lett. 32 131Google Scholar

    [53]

    Iakymchuk T, Rosado Muñoz A, Guerrero Martínez J F, Bataller Mompeán M, Francés Víllora J V 2015 Eurasip J. Image Video Process. 2015 4Google Scholar

    [54]

    Cho S G, Beigne E, Zhang Z 2019 2019 IEEE Custom Integrated Circuits Conference (CICC) Austin, TX, USA, April 14–17, 2019 p1

    [55]

    Yan X, Zhao J, Liu S, Zhou Z, Liu Q, Chen J, Liu X Y 2018 Adv. Funct. Mater. 28 1705320Google Scholar

    [56]

    Yan X, Qin C, Lu C, Zhao J, Zhao R, Ren D, Zhou Z, Wang H, Wang J, Zhang L, Li X, Pei Y, Wang G, Zhao Q, Wang K, Xiao Z, Li H 2019 ACS Appl. Mater. Interfaces 11 48029Google Scholar

    [57]

    Yan X, Wang K, Zhao J, Zhou Z, Wang H, Wang J, Zhang L, Li X, Xiao Z, Zhao Q, Pei Y, Wang G, Qin C, Li H, Lou J, Liu Q, Zhou P 2019 Small 15 1900107Google Scholar

    [58]

    Lee T H, Hwang H G, Woo J U, Kim D H, Kim T W, Nahm S 2018 ACS Appl. Mater. Interfaces 10 25673Google Scholar

    [59]

    Wang Z, Joshi S, Savel'ev S E, Jiang H, Midya R, Lin P, Hu M, Ge N, Strachan J P, Li Z, Wu Q, Barnell M, Li G L, Xin H L, Williams R S, Xia Q, Yang J J 2017 Nat. Mater. 16 101Google Scholar

    [60]

    Yang J T, Ge C, Du J Y, Huang H Y, He M, Wang C, Lu H B, Yang G Z, Jin K J 2018 Adv. Mater. 30 1801548Google Scholar

    [61]

    Li Y, Lu J, Shang D, Liu Q, Wu S, Wu Z, Zhang X, Yang J, Wang Z, Lv H, Liu M 2020 Adv. Mater. 32 2003018Google Scholar

    [62]

    Mukherjee A, Sagar S, Parveen S, Das B C 2021 Appl. Phys. Lett. 119 253502Google Scholar

    [63]

    Liang F X, Wang I T, Hou T H 2021 Adv. Intell. Syst. 3 2100007Google Scholar

    [64]

    Zhang X, Wang W, Liu Q, Zhao X, Wei J, Cao R, Yao Z, Zhu X, Zhang F, Lv H, Long S, Liu M 2018 IEEE Electron Device Lett. 39 308Google Scholar

    [65]

    Duan Q, Jing Z, Zou X, Wang Y, Yang K, Zhang T, Wu S, Huang R, Yang Y 2020 Nat. Commun. 11 3399Google Scholar

    [66]

    Lu Y F, Li Y, Li H, Wan T Q, Huang X, He Y H, Miao X 2020 IEEE Electron Device Lett. 41 1245Google Scholar

    [67]

    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

    [68]

    Wang Y, Chen X, Shen D, Zhang M, Chen X, Chen X, Shao W, Gu H, Xu J, Hu E, Wang L, Xu R, Tong Y 2021 Nanomaterials 11 2860Google Scholar

    [69]

    Bousoulas P, Panagopoulou M, Boukos N, Tsoukalas D 2021 J. Phys. D:Appl. Phys. 54 225303Google Scholar

    [70]

    Han J K, Oh J, Yun G J, Yoo D, Kim M S, Yu J M, Choi S Y, Choi Y K 2021 Sci. Adv. 7 eabg8836Google Scholar

    [71]

    Wan T, Ma S, Liao F, Fan L, Chai Y 2022 Sci. China Inf. Sci. 65 141401Google Scholar

    [72]

    Wang T Y, Meng J L, Li Q X, He Z Y, Zhu H, Ji L, Sun Q Q, Chen L, Zhang D W 2021 Nano Energy 89 106291Google Scholar

    [73]

    Meng J, Wang T, Zhu H, Ji L, Bao W, Zhou P, Chen L, Sun Q Q, Zhang D W 2022 Nano Lett. 22 81Google Scholar

    [74]

    Seo S, Jo S H, Kim S, Shim J, Oh S, Kim J H, Heo K, Choi J W, Choi C, Oh S, Kuzum D, Wong H P, Park J H 2018 Nat. Commun. 9 5106Google Scholar

    [75]

    Liao F, Zhou Z, Kim B J, Chen J, Wang J, Wan T, Zhou Y, Hoang A T, Wang C, Kang J, Ahn J H, Chai Y 2022 Nat. Electron. 5 84Google Scholar

    [76]

    Gao S, Liu G, Yang H, Hu C, Chen Q, Gong G, Xue W, Yi X, Shang J, Li R W 2019 ACS Nano 13 2634Google Scholar

    [77]

    Tan H, Tao Q, Pande I, Majumdar S, Liu F, Zhou Y, Persson P O A, Rosen J, van Dijken S 2020 Nat. Commun. 11 1369Google Scholar

    [78]

    Zhou F, Zhou Z, Chen J, Choy T H, Wang J, Zhang N, Lin Z, Yu S, Kang J, Wong H S P, Chai Y 2019 Nat. Nanotechnol. 14 776Google Scholar

    [79]

    Liu L, Cheng Z, Jiang B, Liu Y, Zhang Y, Yang F, Wang J, Yu X F, Chu P K, Ye C 2021 ACS Appl. Mater. Interfaces 13 30797Google Scholar

    [80]

    Zhou F, Chen J, Tao X, Wang X, Chai Y 2019 Research 2019 9490413

    [81]

    Xiang D, Liu T, Xu J, Tan J Y, Hu Z, Lei B, Zheng Y, Wu J, Neto A H C, Liu L, Chen W 2018 Nat. Commun. 9 2966Google Scholar

    [82]

    Zhang Z, Wang S, Liu C, Xie R, Hu W, Zhou P 2022 Nat. Nanotechnol. 17 27Google Scholar

    [83]

    Wang S, Chen C, Yu Z, He Y, Chen X, Wan Q, Shi Y, Zhang D W, Zhou H, Wang X, Zhou P 2019 Adv. Mater. 31 1806227Google Scholar

    [84]

    Zhu Q B, Li B, Yang D D, Liu C, Feng S, Chen M L, Sun Y, Tian Y N, Su X, Wang X M, Qiu S, Li Q W, Li X M, Zeng H B, Cheng H M, Sun D M 2021 Nat. Commun. 12 1798Google Scholar

    [85]

    Hu L, Yang J, Wang J, Cheng P, Chua L O, Zhuge F 2021 Adv. Funct. Mater. 31 2005582Google Scholar

    [86]

    Hou Y X, Li Y, Zhang Z C, Li J Q, Qi D H, Chen X D, Wang J J, Yao B W, Yu M X, Lu T B, Zhang J 2021 ACS Nano 15 1497Google Scholar

    [87]

    Yang L, Singh M, Shen S W, Chih K Y, Liu S W, Wu C I, Chu C W, Lin H W 2020 Adv. Funct. Mater. 31 2008259Google Scholar

    [88]

    Pei Y, Yan L, Wu Z, Lu J, Zhao J, Chen J, Liu Q, Yan X 2021 ACS Nano 15 17319Google Scholar

    [89]

    John R A, Acharya J, Zhu C, Surendran A, Bose S K, Chaturvedi A, Tiwari N, Gao Y, He Y, Zhang K K, Xu M, Leong W L, Liu Z, Basu A, Mathews N 2020 Nat. Commun. 11 3211Google Scholar

    [90]

    Egmont Petersen M, De Ridder D, Handels H 2002 Pattern Recognit. 35 2279Google Scholar

    [91]

    Rao D H, Panduranga P P 2006 2006 IEEE International Conference on Industrial Technology Mumbai, India, December 15–17, 2006 p2821

    [92]

    Chakraborty D, Raj S, Fernandes S L, Jha S K 2019 IEEE J. Emerging Sel. Top. Circuits Syst. 9 580Google Scholar

    [93]

    Pannu J S, Raj S, Fernandes S L, Chakraborty D, Rafiq S, Cady N, Jha S K 2020 IEEE Trans. Circuits Syst. II Express Briefs 67 961Google Scholar

    [94]

    Mannion D J, Mehonic A, Ng W H, Kenyon A J 2019 Front. Neurosci. 13 1386Google Scholar

    [95]

    Li C, Hu M, Li Y, Jiang H, Ge N, Montgomery E, Zhang J, Song W, Dávila N, Graves C E, Li Z, Strachan J P, Lin P, Wang Z, Barnell M, Wu Q, Williams R S, Yang J J, Xia Q 2017 Nat. Electron. 1 52

    [96]

    Lin P, Li C, Wang Z, Li Y, Jiang H, Song W, Rao M, Zhuo Y, Upadhyay N K, Barnell M, Wu Q, Yang J J, Xia Q 2020 Nat. Electron. 3 225Google Scholar

    [97]

    Pajouhi Z, Roy K 2018 IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37 1774Google Scholar

    [98]

    Yu Y, Deng Q, Ren L, Tashi N 2020 Neural Process. Lett. 51 1891Google Scholar

    [99]

    Bao L, Kang J, Fang Y, Yu Z, Wang Z, Yang Y, Cai Y, Huang R 2018 Sci. Rep. 8 13727Google Scholar

    [100]

    Hu W C, Yang C Y, Huang D Y 2011 J. Visual Commun. Image Represent. 22 543Google Scholar

    [101]

    Somasundaram G, Sivalingam R, Morellas V, Papanikolopoulos N 2013 IEEE Trans. Intell. Transp. Syst. 14 69Google Scholar

    [102]

    Huang K, Wang L, Tan T, Maybank S 2008 Pattern Recognit. 41 432Google Scholar

    [103]

    Maan A K, Kumar D S, Sugathan S, James A P 2015 IEEE Trans. Very Large Scale Integr. VLSI Syst. 23 2337Google Scholar

    [104]

    Jayachandran D, Oberoi A, Sebastian A, Choudhury T H, Shankar B, Redwing J M, Das S 2020 Nat. Electron. 3 646Google Scholar

    [105]

    Wang Y, Gong Y, Huang S, Xing X, Lv Z, Wang J, Yang J Q, Zhang G, Zhou Y, Han S T 2021 Nat. Commun. 12 5979Google Scholar

    [106]

    Russo F 2002 IEEE Trans. Instrum. Meas. 51 824Google Scholar

    [107]

    Wang C Y, Liang S J, Wang S, Wang P, Li Z a, Wang Z, Gao A, Pan C, Liu C, Liu J, Yang H, Liu X, Song W, Wang C, Cheng B, Wang X, Chen K, Wang Z, Watanabe K, Taniguchi T, Yang J J, Miao F 2020 Sci. Adv. 6 eaba6173Google Scholar

    [108]

    Yang X, Xiong Z, Chen Y, Ren Y, Zhou L, Li H, Zhou Y, Pan F, Han S T 2020 Nano Energy 78 105246Google Scholar

    [109]

    Zhu R, Tang Z, Ye S, Huang Q, Guo L, Chang S 2021 IEEE Trans. Electron Devices 68 602Google Scholar

    [110]

    Tang Z, Zhu R, Hu R, Chen Y, Wu E Q, Wang H, He J, Huang Q, Chang S 2021 IEEE Trans. Cognit. Dev. Syst. 13 645Google Scholar

    [111]

    Xin M, Wang Y 2019 Eurasip J. Image Video Process. 2019 40Google Scholar

    [112]

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

    [113]

    Bouvier M, Valentian A, Mesquida T, Rummens F, Reyboz M, Vianello E, Beigne E 2019 ACM J. Emerging Technol. Comput. Syst. 15 1

    [114]

    Boybat I, Le Gallo M, Nandakumar S R, Moraitis T, Parnell T, Tuma T, Rajendran B, Leblebici Y, Sebastian A, Eleftheriou E 2018 Nat. Commun. 9 2514Google Scholar

    [115]

    Wang Z, Joshi S, Savel’Ev S, Song W, Midya R, Li Y, Rao M, Yan P, Asapu S, Zhuo Y, Jiang H, Lin P, Li C, Yoon J H, Upadhyay N K, Zhang J, Hu M, Strachan J P, Barnell M, Wu Q, Wu H, Williams R S, Xia Q, Yang J J 2018 Nat. Electron. 1 137Google Scholar

    [116]

    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

  • [1] 杨光, 钞苏亚, 聂敏, 刘原华, 张美玲. 面向图像分类的混合量子长短期记忆神经网络构建方法. 物理学报, 2023, 72(5): 058901. doi: 10.7498/aps.72.20221924
    [2] 徐子恒, 何玉珠, 康艳梅. 基于随机放电神经元网络的彩色图像感知研究. 物理学报, 2022, 71(7): 070501. doi: 10.7498/aps.71.20211982
    [3] 沈柳枫, 胡令祥, 康逢文, 叶羽敏, 诸葛飞. 光电神经形态器件及其应用. 物理学报, 2022, 71(14): 148505. doi: 10.7498/aps.71.20220111
    [4] 张海燕, 徐心语, 马雪芬, 朱琦, 彭丽. 超声图像中复合材料褶皱形态的Mask-RCNN识别方法. 物理学报, 2022, 71(7): 074302. doi: 10.7498/aps.71.20212009
    [5] 任宽, 张珂嘉, 秦溪子, 任焕鑫, 朱守辉, 杨峰, 孙柏, 赵勇, 张勇. 基于忆容器件的神经形态计算研究进展. 物理学报, 2021, 70(7): 078701. doi: 10.7498/aps.70.20201632
    [6] 周静, 张晓芳, 赵延庚. 一种基于图像融合和卷积神经网络的相位恢复方法. 物理学报, 2021, 70(5): 054201. doi: 10.7498/aps.70.20201362
    [7] 姚军财, 申静. 基于图像内容对比感知的图像质量客观评价. 物理学报, 2020, 69(14): 148702. doi: 10.7498/aps.69.20200335
    [8] 姚军财, 刘贵忠. 基于图像内容视觉感知的图像质量客观评价方法. 物理学报, 2018, 67(10): 108702. doi: 10.7498/aps.67.20180168
    [9] 王殿伟, 韩鹏飞, 范九伦, 刘颖, 许志杰, 王晶. 基于光照-反射成像模型和形态学操作的多谱段图像增强算法. 物理学报, 2018, 67(21): 210701. doi: 10.7498/aps.67.20181288
    [10] 郭业才, 周林锋. 基于脉冲耦合神经网络和图像熵的各向异性扩散模型研究. 物理学报, 2015, 64(19): 194204. doi: 10.7498/aps.64.194204
    [11] 陈勇, 郭隆德, 彭强, 陈志强, 刘卫红. 低速湍流模拟的预处理技术研究. 物理学报, 2015, 64(13): 134701. doi: 10.7498/aps.64.134701
    [12] 路志英, 刘海, 贾惠珍, 尹静. 基于雷达反射率图像特征的冰雹暴雨识别. 物理学报, 2014, 63(18): 189201. doi: 10.7498/aps.63.189201
    [13] 刘玉东, 王连明. 基于忆阻器的spiking神经网络在图像边缘提取中的应用. 物理学报, 2014, 63(8): 080503. doi: 10.7498/aps.63.080503
    [14] 姚畅, 陈后金, Yang Yong-Yi, 李艳凤, 韩振中, 张胜君. 基于自适应核学习相关向量机的乳腺X线图像微钙化点簇处理方法研究. 物理学报, 2013, 62(8): 088702. doi: 10.7498/aps.62.088702
    [15] 赵文达, 赵建, 续志军. 基于结构张量的变分多源图像融合. 物理学报, 2013, 62(21): 214204. doi: 10.7498/aps.62.214204
    [16] 高向东, 龙观富, 汪润林, Katayama Seiji. 大功率盘形激光焊飞溅特征分析. 物理学报, 2012, 61(9): 098103. doi: 10.7498/aps.61.098103
    [17] 季超, 张凌云, 窦硕星, 王鹏业. 原子力显微镜观测生物大分子图像的一种处理方法. 物理学报, 2011, 60(9): 098703. doi: 10.7498/aps.60.098703
    [18] 王 熠, 翟宏琛, 母国光. 基于形态矩阵的图像模糊匹配方法. 物理学报, 2005, 54(5): 1965-1968. doi: 10.7498/aps.54.1965
    [19] 宋菲君, 赵文杰, S. Jutamulia, 宋建力, 姚思一, 王 栋. Haar-Gaussian小波变换在边缘测量中的应用. 物理学报, 2003, 52(12): 3055-3060. doi: 10.7498/aps.52.3055
    [20] 阳世新, 李方华, 刘玉东, 古元新, 范海福. 直接法应用于蛋白质二维晶体的电子晶体学图像处理. 物理学报, 2000, 49(10): 1982-1987. doi: 10.7498/aps.49.1982
计量
  • 文章访问数:  7767
  • PDF下载量:  420
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-03-15
  • 修回日期:  2022-04-05
  • 上网日期:  2022-07-21
  • 刊出日期:  2022-07-20

/

返回文章
返回