Application of neuromorphic resistive random access memory in image processing

Jiang Bi-Yi; Zhou Fei-Chi; Chai Yang

doi:10.7498/aps.71.20220463

Abstract

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.

Keywords:

neuromorphic resistive random access memory /
image preprocessing /
image recognition

Authors and contacts

1.
School of Microelectronics, Southern University of Science and Technology, Shenzhen 518000, China
2.
Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong 999077, China

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).

FullText HTML : translate this paragraph

References

[1]	Ma Y, Wu J, Long C, Lin Y B 2021 IEEE Internet Things J. 9 2802 Google 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 30 235 Google 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 2000224 Google Scholar
[12]	Webster M A 1996 Netw. Comput. Neural Syst. 7 587 Google Scholar
[13]	Sabesan R, Schmidt Brian P, Tuten William S, Roorda A 2016 Sci. Adv. 2 e1600797 Google Scholar
[14]	Cheng Z, Ríos C, Pernice W H P, Wright C D, Bhaskaran H 2017 Sci. Adv. 3 e1700160 Google Scholar
[15]	Zhu X, Lu W D 2018 ACS Nano 12 1242 Google Scholar
[16]	Liao F, Zhou F, Chai Y 2021 J. Semicond. 42 013105 Google Scholar
[17]	Chai Y 2020 Nature 579 32 Google Scholar
[18]	Li C, Guo J, Porikli F, Pang Y 2018 Pattern Recognit. Lett. 104 15 Google Scholar
[19]	Khan M Z, Harous S, Hassan S U, Ghani Khan M U, Iqbal R, Mumtaz S 2019 IEEE Access 7 72622 Google 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. 6 198 Google Scholar
[22]	Shi T, Wang R, Wu Z, Sun Y, An J, Liu Q 2021 Small Struct. 2 2000109 Google Scholar
[23]	Yang Y, Gao P, Gaba S, Chang T, Pan X, Lu W 2012 Nat. Commun. 3 732 Google 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 4097 Google 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. 17 335 Google Scholar
[26]	Chandrasekaran S, Simanjuntak F M, Saminathan R, Panda D, Tseng T Y 2019 Nanotechnology 30 445205 Google 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 6100 Google Scholar
[28]	Dash C S, Sahoo S, Prabaharan S R S 2018 Solid State Ionics 324 218 Google 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 505210 Google Scholar
[30]	Simanjuntak F M, Chandrasekaran S, Lin C C, Tseng T Y 2019 APL Mater. 7 051108 Google 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 3671 Google Scholar
[32]	Wu W, Wu H, Gao B, Deng N, Yu S, Qian H 2017 IEEE Electron Device Lett. 38 1019 Google 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 24503 Google 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 3936 Google 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 1804170 Google 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 2100041 Google Scholar
[37]	Bayat F M, Prezioso M, Chakrabarti B, Nili H, Kataeva I, Strukov D 2018 Nat. Commun. 9 2331 Google Scholar
[38]	Sheridan P M, Cai F, Du C, Ma W, Zhang Z, Lu W D 2017 Nat. Nanotechnol. 12 784 Google 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 15199 Google 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 49 Google 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 2002251 Google 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 1705193 Google 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. 28 356 Google 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 2294 Google Scholar
[48]	Farsa E Z, Ahmadi A, Maleki M A, Gholami M, Rad H N 2019 IEEE Trans. Circuits Syst. II Express Briefs 66 1582 Google 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 105689 Google Scholar
[51]	Tsodyks M V, Markram H 1997 Proc. Natl. Acad. Sci. USA 94 719 Google Scholar
[52]	Meftah B, Lezoray O, Benyettou A 2010 Neural Process. Lett. 32 131 Google 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 4 Google 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 1705320 Google 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 48029 Google 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 1900107 Google 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 25673 Google 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. 16 101 Google 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 1801548 Google 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 2003018 Google Scholar
[62]	Mukherjee A, Sagar S, Parveen S, Das B C 2021 Appl. Phys. Lett. 119 253502 Google Scholar
[63]	Liang F X, Wang I T, Hou T H 2021 Adv. Intell. Syst. 3 2100007 Google 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. 39 308 Google Scholar
[65]	Duan Q, Jing Z, Zou X, Wang Y, Yang K, Zhang T, Wu S, Huang R, Yang Y 2020 Nat. Commun. 11 3399 Google 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 1245 Google 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 3208 Google 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 2860 Google Scholar
[69]	Bousoulas P, Panagopoulou M, Boukos N, Tsoukalas D 2021 J. Phys. D:Appl. Phys. 54 225303 Google 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 eabg8836 Google Scholar
[71]	Wan T, Ma S, Liao F, Fan L, Chai Y 2022 Sci. China Inf. Sci. 65 141401 Google 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 106291 Google 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 81 Google 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 5106 Google 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 84 Google 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 2634 Google 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 1369 Google 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. 14 776 Google 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 30797 Google 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 2966 Google Scholar
[82]	Zhang Z, Wang S, Liu C, Xie R, Hu W, Zhou P 2022 Nat. Nanotechnol. 17 27 Google 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 1806227 Google 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 1798 Google Scholar
[85]	Hu L, Yang J, Wang J, Cheng P, Chua L O, Zhuge F 2021 Adv. Funct. Mater. 31 2005582 Google 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 1497 Google 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 2008259 Google Scholar
[88]	Pei Y, Yan L, Wu Z, Lu J, Zhao J, Chen J, Liu Q, Yan X 2021 ACS Nano 15 17319 Google 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 3211 Google Scholar
[90]	Egmont Petersen M, De Ridder D, Handels H 2002 Pattern Recognit. 35 2279 Google 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. 9 580 Google 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 67 961 Google Scholar
[94]	Mannion D J, Mehonic A, Ng W H, Kenyon A J 2019 Front. Neurosci. 13 1386 Google 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. 3 225 Google Scholar
[97]	Pajouhi Z, Roy K 2018 IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37 1774 Google Scholar
[98]	Yu Y, Deng Q, Ren L, Tashi N 2020 Neural Process. Lett. 51 1891 Google Scholar
[99]	Bao L, Kang J, Fang Y, Yu Z, Wang Z, Yang Y, Cai Y, Huang R 2018 Sci. Rep. 8 13727 Google Scholar
[100]	Hu W C, Yang C Y, Huang D Y 2011 J. Visual Commun. Image Represent. 22 543 Google Scholar
[101]	Somasundaram G, Sivalingam R, Morellas V, Papanikolopoulos N 2013 IEEE Trans. Intell. Transp. Syst. 14 69 Google Scholar
[102]	Huang K, Wang L, Tan T, Maybank S 2008 Pattern Recognit. 41 432 Google Scholar
[103]	Maan A K, Kumar D S, Sugathan S, James A P 2015 IEEE Trans. Very Large Scale Integr. VLSI Syst. 23 2337 Google Scholar
[104]	Jayachandran D, Oberoi A, Sebastian A, Choudhury T H, Shankar B, Redwing J M, Das S 2020 Nat. Electron. 3 646 Google 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 5979 Google Scholar
[106]	Russo F 2002 IEEE Trans. Instrum. Meas. 51 824 Google 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 eaba6173 Google 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 105246 Google Scholar
[109]	Zhu R, Tang Z, Ye S, Huang Q, Guo L, Chang S 2021 IEEE Trans. Electron Devices 68 602 Google 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. 13 645 Google Scholar
[111]	Xin M, Wang Y 2019 Eurasip J. Image Video Process. 2019 40 Google Scholar
[112]	Yao P, Wu H, Gao B, Tang J, Zhang Q, Zhang W, Yang J J, Qian H 2020 Nature 577 641 Google 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 2514 Google 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. 1 137 Google 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. 15 776 Google Scholar

Cited By

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

Figure 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).

DownLoad: Full-Size Img PowerPoint

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]

Figure 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].

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

图 7 ITO/MoO_x/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

Figure 7. ITO/MoO_x/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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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V₀随V_th端口输入信号和input端口输入信号的变化

Figure 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 V_th port and input port

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

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

Figure 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.

DownLoad: Full-Size Img PowerPoint

图 15 基于ITO/MoO_x/Pd ORRAM阵列实现的图像锐化^[78]　(a) 8$ \times $8 ITO/MoO_x/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

Figure 15. ITO/MoO_x/Pd ORRAM array for image sharpening^[78]: (a) 8$ \times $8 ITO/MoO_x/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.

DownLoad: Full-Size Img PowerPoint

map

[1]	Ma Y, Wu J, Long C, Lin Y B 2021 IEEE Internet Things J. 9 2802 Google 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 30 235 Google 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 2000224 Google Scholar
[12]	Webster M A 1996 Netw. Comput. Neural Syst. 7 587 Google Scholar
[13]	Sabesan R, Schmidt Brian P, Tuten William S, Roorda A 2016 Sci. Adv. 2 e1600797 Google Scholar
[14]	Cheng Z, Ríos C, Pernice W H P, Wright C D, Bhaskaran H 2017 Sci. Adv. 3 e1700160 Google Scholar
[15]	Zhu X, Lu W D 2018 ACS Nano 12 1242 Google Scholar
[16]	Liao F, Zhou F, Chai Y 2021 J. Semicond. 42 013105 Google Scholar
[17]	Chai Y 2020 Nature 579 32 Google Scholar
[18]	Li C, Guo J, Porikli F, Pang Y 2018 Pattern Recognit. Lett. 104 15 Google Scholar
[19]	Khan M Z, Harous S, Hassan S U, Ghani Khan M U, Iqbal R, Mumtaz S 2019 IEEE Access 7 72622 Google 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. 6 198 Google Scholar
[22]	Shi T, Wang R, Wu Z, Sun Y, An J, Liu Q 2021 Small Struct. 2 2000109 Google Scholar
[23]	Yang Y, Gao P, Gaba S, Chang T, Pan X, Lu W 2012 Nat. Commun. 3 732 Google 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 4097 Google 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. 17 335 Google Scholar
[26]	Chandrasekaran S, Simanjuntak F M, Saminathan R, Panda D, Tseng T Y 2019 Nanotechnology 30 445205 Google 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 6100 Google Scholar
[28]	Dash C S, Sahoo S, Prabaharan S R S 2018 Solid State Ionics 324 218 Google 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 505210 Google Scholar
[30]	Simanjuntak F M, Chandrasekaran S, Lin C C, Tseng T Y 2019 APL Mater. 7 051108 Google 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 3671 Google Scholar
[32]	Wu W, Wu H, Gao B, Deng N, Yu S, Qian H 2017 IEEE Electron Device Lett. 38 1019 Google 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 24503 Google 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 3936 Google 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 1804170 Google 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 2100041 Google Scholar
[37]	Bayat F M, Prezioso M, Chakrabarti B, Nili H, Kataeva I, Strukov D 2018 Nat. Commun. 9 2331 Google Scholar
[38]	Sheridan P M, Cai F, Du C, Ma W, Zhang Z, Lu W D 2017 Nat. Nanotechnol. 12 784 Google 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 15199 Google 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 49 Google 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 2002251 Google 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 1705193 Google 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. 28 356 Google 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 2294 Google Scholar
[48]	Farsa E Z, Ahmadi A, Maleki M A, Gholami M, Rad H N 2019 IEEE Trans. Circuits Syst. II Express Briefs 66 1582 Google 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 105689 Google Scholar
[51]	Tsodyks M V, Markram H 1997 Proc. Natl. Acad. Sci. USA 94 719 Google Scholar
[52]	Meftah B, Lezoray O, Benyettou A 2010 Neural Process. Lett. 32 131 Google 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 4 Google 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 1705320 Google 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 48029 Google 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 1900107 Google 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 25673 Google 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. 16 101 Google 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 1801548 Google 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 2003018 Google Scholar
[62]	Mukherjee A, Sagar S, Parveen S, Das B C 2021 Appl. Phys. Lett. 119 253502 Google Scholar
[63]	Liang F X, Wang I T, Hou T H 2021 Adv. Intell. Syst. 3 2100007 Google 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. 39 308 Google Scholar
[65]	Duan Q, Jing Z, Zou X, Wang Y, Yang K, Zhang T, Wu S, Huang R, Yang Y 2020 Nat. Commun. 11 3399 Google 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 1245 Google 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 3208 Google 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 2860 Google Scholar
[69]	Bousoulas P, Panagopoulou M, Boukos N, Tsoukalas D 2021 J. Phys. D:Appl. Phys. 54 225303 Google 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 eabg8836 Google Scholar
[71]	Wan T, Ma S, Liao F, Fan L, Chai Y 2022 Sci. China Inf. Sci. 65 141401 Google 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 106291 Google 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 81 Google 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 5106 Google 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 84 Google 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 2634 Google 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 1369 Google 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. 14 776 Google 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 30797 Google 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 2966 Google Scholar
[82]	Zhang Z, Wang S, Liu C, Xie R, Hu W, Zhou P 2022 Nat. Nanotechnol. 17 27 Google 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 1806227 Google 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 1798 Google Scholar
[85]	Hu L, Yang J, Wang J, Cheng P, Chua L O, Zhuge F 2021 Adv. Funct. Mater. 31 2005582 Google 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 1497 Google 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 2008259 Google Scholar
[88]	Pei Y, Yan L, Wu Z, Lu J, Zhao J, Chen J, Liu Q, Yan X 2021 ACS Nano 15 17319 Google 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 3211 Google Scholar
[90]	Egmont Petersen M, De Ridder D, Handels H 2002 Pattern Recognit. 35 2279 Google 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. 9 580 Google 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 67 961 Google Scholar
[94]	Mannion D J, Mehonic A, Ng W H, Kenyon A J 2019 Front. Neurosci. 13 1386 Google 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. 3 225 Google Scholar
[97]	Pajouhi Z, Roy K 2018 IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37 1774 Google Scholar
[98]	Yu Y, Deng Q, Ren L, Tashi N 2020 Neural Process. Lett. 51 1891 Google Scholar
[99]	Bao L, Kang J, Fang Y, Yu Z, Wang Z, Yang Y, Cai Y, Huang R 2018 Sci. Rep. 8 13727 Google Scholar
[100]	Hu W C, Yang C Y, Huang D Y 2011 J. Visual Commun. Image Represent. 22 543 Google Scholar
[101]	Somasundaram G, Sivalingam R, Morellas V, Papanikolopoulos N 2013 IEEE Trans. Intell. Transp. Syst. 14 69 Google Scholar
[102]	Huang K, Wang L, Tan T, Maybank S 2008 Pattern Recognit. 41 432 Google Scholar
[103]	Maan A K, Kumar D S, Sugathan S, James A P 2015 IEEE Trans. Very Large Scale Integr. VLSI Syst. 23 2337 Google Scholar
[104]	Jayachandran D, Oberoi A, Sebastian A, Choudhury T H, Shankar B, Redwing J M, Das S 2020 Nat. Electron. 3 646 Google 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 5979 Google Scholar
[106]	Russo F 2002 IEEE Trans. Instrum. Meas. 51 824 Google 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 eaba6173 Google 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 105246 Google Scholar
[109]	Zhu R, Tang Z, Ye S, Huang Q, Guo L, Chang S 2021 IEEE Trans. Electron Devices 68 602 Google 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. 13 645 Google Scholar
[111]	Xin M, Wang Y 2019 Eurasip J. Image Video Process. 2019 40 Google Scholar
[112]	Yao P, Wu H, Gao B, Tang J, Zhang Q, Zhang W, Yang J J, Qian H 2020 Nature 577 641 Google 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 2514 Google 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. 1 137 Google 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. 15 776 Google Scholar

[1]	Yang Guang, Chao Su-Ya, Nie Min, Liu Yuan-Hua, Zhang Mei-Ling. Construction method of hybrid quantum long-short term memory neural network for image classification. Acta Physica Sinica, 2023, 72(5): 058901. doi: 10.7498/aps.72.20221924
[2]	Xu Zi-Heng, He Yu-Zhu, Kang Yan-Mei. Color image perception based on stochastic spiking neural network. Acta Physica Sinica, 2022, 71(7): 070501. doi: 10.7498/aps.71.20211982
[3]	Shen Liu-Feng, Hu Ling-Xiang, Kang Feng-Wen, Ye Yu-Min, Zhuge Fei. Optoelectronic neuromorphic devices and their applications. Acta Physica Sinica, 2022, 71(14): 148505. doi: 10.7498/aps.71.20220111
[4]	Zhang Hai-Yan, Xu Xin-Yu, Ma Xue-Fen, Zhu Qi, Peng Li. Mask-RCNN recognition method of composite fold shape in ultrasound images. Acta Physica Sinica, 2022, 71(7): 074302. doi: 10.7498/aps.71.20212009
[5]	Ren Kuan, Zhang Ke-Jia, Qin Xi-Zi, Ren Huan-Xin, Zhu Shou-Hui, Yang Feng, Sun Bai, Zhao Yong, Zhang Yong. Research progress of neuromorphic computation based on memcapacitors. Acta Physica Sinica, 2021, 70(7): 078701. doi: 10.7498/aps.70.20201632
[6]	Zhou Jing, Zhang Xiao-Fang, Zhao Yan-Geng. Phase retrieval wavefront sensing based on image fusion and convolutional neural network. Acta Physica Sinica, 2021, 70(5): 054201. doi: 10.7498/aps.70.20201362
[7]	Yao Jun-Cai, Shen Jing. Objective assessment of image quality based on image content contrast perception. Acta Physica Sinica, 2020, 69(14): 148702. doi: 10.7498/aps.69.20200335
[8]	Yao Jun-Cai, Liu Gui-Zhong. Objective assessment method of image quality based on visual perception of image content. Acta Physica Sinica, 2018, 67(10): 108702. doi: 10.7498/aps.67.20180168
[9]	Wang Dian-Wei^1\2, Han Peng-Fei, Fan Jiu-Lun, Liu Ying^1\2, Xu Zhi-Jie, Wang Jing. Multispectral image enhancement based on illuminance-reflection imaging model and morphology operation. Acta Physica Sinica, 2018, 67(21): 210701. doi: 10.7498/aps.67.20181288
[10]	Guo Ye-Cai, Zhou Lin-Feng. Study of anisotropic diffusion model based on pulse coupled neural network and image entropy. Acta Physica Sinica, 2015, 64(19): 194204. doi: 10.7498/aps.64.194204
[11]	Chen Yong, Guo Long-De, Peng Qiang, Chen Zhi-Qiang, Liu Wei-Hong. Study of precondition for simulating low-speed turbulence. Acta Physica Sinica, 2015, 64(13): 134701. doi: 10.7498/aps.64.134701
[12]	Lu Zhi-Ying, Liu Hai, Jia Hui-Zhen, Yin Jing. Recognition of hail and rainstorm based on the radar reflectivity image features. Acta Physica Sinica, 2014, 63(18): 189201. doi: 10.7498/aps.63.189201
[13]	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
[14]	Yao Chang, Chen Hou-Jin, Yang Yong-Yi, Li Yan-Feng, Han Zhen-Zhong, Zhang Sheng-Jun. Microcalcification clusters processing in mammograms based on relevance vector machine with adaptive kernel learning. Acta Physica Sinica, 2013, 62(8): 088702. doi: 10.7498/aps.62.088702
[15]	Zhao Wen-Da, Zhao Jian, Xu Zhi-Jun. Variational multi-source image fusion based on the structure tensor. Acta Physica Sinica, 2013, 62(21): 214204. doi: 10.7498/aps.62.214204
[16]	Gao Xiang-Dong, Long Guan-Fu, Wang Run-Lin, Katayama Seiji. Analysis of characteristics of spatters during high-power disk laser welding. Acta Physica Sinica, 2012, 61(9): 098103. doi: 10.7498/aps.61.098103
[17]	Ji Chao, Zhang Ling-Yun, Dou Shuo-Xing, Wang Peng-Ye. A new method to deal with biomacromolecularimage observed by atomic force microscopy. Acta Physica Sinica, 2011, 60(9): 098703. doi: 10.7498/aps.60.098703
[18]	Wang Yi, Zhai Hong-Chen, Mu Guo-Guang. Fuzzy matching of images based on shape description matrix. Acta Physica Sinica, 2005, 54(5): 1965-1968. doi: 10.7498/aps.54.1965
[19]	Song Fei-Jun, Zhao Wen-Jie, S. Jutamulia, Song Jian-Li, Yao Si-Yi, Wang Dong. Application of Haar-Gaussian wavelet transform to edge-detection. Acta Physica Sinica, 2003, 52(12): 3055-3060. doi: 10.7498/aps.52.3055
[20]	YANG SHI-XIN, LI FANG-HUA, LIU YU-DONG, GU YUAN-XIN, FAN HAI-FU. APPLICATION OF DIRECT METHOD TO ELECTRON CRYSTALLOGRAPHIC IMAGE PROCESSING FOR T WO-DIMENSIONAL PROTEIN CRYSTALS. Acta Physica Sinica, 2000, 49(10): 1982-1987. doi: 10.7498/aps.49.1982

Catalog

Vol. 71, Issue 14 - 2022-07-20

Metrics

Abstract views: 7785
PDF Downloads: 421
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板