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Memristive Devices: Analog–Digital Hybrid Memristive Devices for Image Pattern Recognition with Tunable Learning Accuracy and Speed (Small Methods 10/2019)
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.201970031
Subject(s) - memristor , crossbar switch , neuromorphic engineering , computer science , modulation (music) , electronic engineering , resistive touchscreen , resistive random access memory , spike timing dependent plasticity , artificial intelligence , materials science , pattern recognition (psychology) , voltage , computer vision , electrical engineering , artificial neural network , engineering , physics , biochemistry , chemistry , receptor , long term potentiation , acoustics
In article number 1900160 by Zhongqiang Wang, Haiyang Xu and co‐workers, an Au/WO 3−x /Ti memristive device is demonstrated that can exhibit hybrid analog and digital resistive switching behavior by relying on interface and bulk conductance modulation, respectively. Using a spike‐timing‐dependent plasticity learning scheme, an effective method is developed to tune learning accuracy and speed of pattern recognition by adjusting the proportion of analog to digital devices in the memristor crossbar array.