A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network

Zhang，Hengjie1,2,3; Jiang，Biyi1,4; Cheng，Chuantong2,3; Huang，Beiju2,3; Zhang，Huan2,3; Chen，Run2,3; Xu，Jiayi1; Huang，Yulong2,3; Chen，Hongda2,3; Pei，Weihua2,3; Chai，Yang4; Zhou，Feichi1

2023-04-26

10.1021/acs.nanolett.2c03624

Nano Letters   影响因子和分区

1530-6984

1530-6992

Two-terminal self-rectifying (SR)-synaptic memristors are preeminent candidates for high-density and efficient neuromorphic computing, especially for future three-dimensional integrated systems, which can self-suppress the sneak path current in crossbar arrays. However, SR-synaptic memristors face the critical challenges of nonlinear weight potentiation and steep depression, hindering their application in conventional artificial neural networks (ANNs). Here, a SR-synaptic memristor (Pt/NiO/WO:Ti/W) and cross-point array with sneak path current suppression features and ultrahigh-weight potentiation linearity up to 0.9997 are introduced. The image contrast enhancement and background filtering are demonstrated on the basis of the device array. Moreover, an unsupervised self-organizing map (SOM) neural network is first developed for orientation recognition with high recognition accuracy (0.98) and training efficiency and high resilience toward both noises and steep synaptic depression. These results solve the challenges of SR memristors in the conventional ANN, extending the possibilities of large-scale oxide SR-synaptic arrays for high-density, efficient, and accurate neuromorphic computing.

image background filtering orientation recognition self-organizing-map (SOM) neural network self-rectifying synaptic memristor array ultrahigh weight potentiation linearity

SCI ; EI

英语

NI论文

第一 ; 通讯

National Natural Science Foundation of China["62104091","52273246","61675191","61904173","61974099","62022081","61634006"] ; National Key R&D Program of China[2018YFA0209000] ; Guangdong Natural Science Founda-tion[2022A1515011064] ; Young Innovative Talent Project Research Program[2021KQNCX077] ; Shenzhen Fundamental Research Program[JCYJ20220530115204009] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences["2018146","2022109"]

Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics

Chemistry, Multidisciplinary ; Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Applied ; Physics, Condensed Matter

WOS:000971989300001

AMER CHEMICAL SOC

20231613900289

Conformal mapping ; Image enhancement ; Memristors

Semiconductor Devices and Integrated Circuits:714.2

MATERIALS SCIENCE

2-s2.0-85152644954

Scopus

1.School of Microelectronics,Southern University of Science and Technology,Shenzhen,518000,China
2.The State Key Laboratory on Integrated Optoelectronics,Institute of Semiconductors,Chinese Academy of Sciences,Beijing,100083,China
3.College of Materials Science and Optoelectronic Technology,University of Chinese Academy of Sciences,Beijing,100049,China
4.Department of Applied Physics,The Hong Kong Polytechnic University,Hong Kong,999077,Hong Kong

Zhang，Hengjie,Jiang，Biyi,Cheng，Chuantong,et al. A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network[J]. Nano Letters,2023,23(8):3107-3115.

Zhang，Hengjie.,Jiang，Biyi.,Cheng，Chuantong.,Huang，Beiju.,Zhang，Huan.,...&Zhou，Feichi.(2023).A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network.Nano Letters,23(8),3107-3115.

Zhang，Hengjie,et al."A Self-Rectifying Synaptic Memristor Array with Ultrahigh Weight Potentiation Linearity for a Self-Organizing-Map Neural Network".Nano Letters 23.8(2023):3107-3115.