摘要
The neuromorphic and in-memory computing using memristors are promising for the building of the next generation computing systems. However, the diffusion dynamics of metal ions/atoms inside the switching medium impose variability in conducting filament (CF) formation, thus limiting their use in von-Neumann architecture. The precise modulation on the diffusion of metal ions/atoms and their reduction/oxidation probability holds promise to overcome the speed, size, and energy issues of present-day computers. Here, this study shows that the diffusion of metal ions can be modulated by defects inside the switching medium and confines metal filaments in a precise 1D channel. This filament confinement by the defect engineering leads to an anomalous switching mechanism with two interchangeable modes: unipolar threshold and bipolar modes. The variation between two modes can be modulated by controlling defects in the structures, leading to a uniform switching with low SET/RESET voltage variations of 17.3% and −17.6%, respectively. Moreover, the convolutional neural network is implemented to emulate synaptic plasticity and image recognition to achieve recognition accuracy of 87% due to a highly linear weight update, demonstrating its potential for in-memory computing.