Flexible TiO2-WO3−x hybrid memristor with enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing
Jianyong Pan, Hao Kan, Zhaorui Liu, Song Gao, Enxiu Wu, Yang Li, Chunwei Zhang
Abstract
Tungsten oxide (WO 3 )-based memristors show promising applications in neuromorphic computing. However, single-layer WO 3 memristors suffer from issues such as weak memory performance and nonlinear conductance variations. In this work, a functional layer based on the hybrids of WO 3−x and TiO 2 is proposed for constructing flexible memristors featuring outstanding synaptic characteristics. Applying diverse electrical stimulations to the memristor enables a range of synaptic functions, elucidating its conduction mechanism through the conductive filament model. The incorporation of TiO 2 not only enhances the memristor’s memory characteristics but makes its conductance more linear, symmetrical and uniform during the long-term changes. Furthermore, in view of the enhanced device performance by TiO 2 doping, the potential of this device for simple behavioral simulation and processing of complex computing problems is explored. The “learning-forgetting-relearning” characteristics and device integrability are visually demonstrated. Applying the device to a convolutional neural network, the recognition accuracy of MNIST handwritten digits reaches 98.7%.