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Analog–Digital Hybrid Memristive Devices for Image Pattern Recognition with Tunable Learning Accuracy and Speed
Author(s) -
Lin Ya,
Wang Cong,
Ren Yanyun,
Wang Zhongqiang,
Xu Haiyang,
Zhao Xiaoning,
Ma Jiangang,
Liu Yichun
Publication year - 2019
Publication title -
small methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.66
H-Index - 46
ISSN - 2366-9608
DOI - 10.1002/smtd.201900160
Subject(s) - computer science , crossbar switch , memristor , neuromorphic engineering , artificial intelligence , artificial neural network , pattern recognition (psychology) , process (computing) , speedup , modulation (music) , electronic engineering , engineering , physics , telecommunications , operating system , acoustics
Brain‐inspired memristive artificial neural networks (ANNs) have been identified as a promising technology for pattern recognition tasks. To optimize the performance of ANNs in various applications, a recognition system with tunable accuracy and speed is highly desirable. A single WO 3− x ‐based memristor is presented in which analog and digital resistive switching (A‐RS and D‐RS) coexist according to a selectively executed forming process. The A‐RS and D‐RS mechanisms can be attributed to the modulation of the Schottky barrier on the interface and the formation/rupture of conducting filaments inside the film, respectively. More importantly, a new analog–digital hybrid ANN is developed based on the coexistence of A‐RS and D‐RS in the WO 3− x memristor, enabling tunable learning accuracy and speed in pattern recognition. The spike‐timing‐dependent plasticity learning rules, as a learning base for image pattern recognition, are demonstrated using A‐RS and D‐RS devices with obviously different fluctuations and rates of change. The learning accuracy/speed can be improved by increasing the proportion of A‐RS/D‐RS in the crossbar array. A convenient method is provided for selecting an optimized pattern recognition scheme to meet different application situations.