Emulating Neuromorphic and In‐Memory Computing Utilizing Defect Engineering in 2D‐Layered WSeO_x and WSe₂ Thin Films by Plasma‐Assisted Selenization Process

Abstract 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.