Analog–Digital Hybrid Memristive Devices for Image Pattern Recognition with Tunable Learning Accuracy and Speed. Issue 10 (26th April 2019) - Lin, Ya; Wang, Cong; Ren, Yanyun; Wang, Zhongqiang; Xu, Haiyang; Zhao, Xiaoning; Ma, Jiangang; Liu, Yichun
Abstract: 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 WO3− 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 WO3− 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. Abstract : An Au/WO3− 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.