Tungsten oxide (WO₃)-based memristors show promising applications in neuromorphic computing. However, single-layer WO₃ 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 TiO₂ 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 TiO₂ 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 TiO₂ 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%.

基于氧化钨 （WO₃） 的忆阻器在神经形态计算中显示出有前途的应用。然而，单层 WO₃ 忆阻器存在内存性能弱和非线性电导变化等问题。在这项工作中，提出了一种基于 WO_3−x 和 TiO₂ 杂化物的功能层，用于构建具有出色突触特性的柔性忆阻器。对忆阻器施加不同的电刺激可实现一系列突触功能，通过导电丝模型阐明其传导机制。TiO₂ 的加入不仅增强了忆阻器的存储特性，而且使其电导在长期变化过程中更加线性、对称和均匀。此外，鉴于 TiO₂ 掺杂增强了器件性能，探索了该器件在简单行为模拟和复杂计算问题处理方面的潜力。直观地展示了 “学习-遗忘-再学习” 特征和设备可集成性。将该设备应用于卷积神经网络，MNIST 手写数字的识别准确率达到 98.7%。