Analog-Digital Hybrid Memristive Devices for Image Pattern Recognition with Tunable Learning Accuracy and Speed

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.