Improvement of analogue switching characteristics of MoS2 memristors through plasma treatment

Memristive devices based on 2D materials, such as WSe2 and MoS2, have been demonstrated to exhibit analogue switching characteristics and enable emulation of ionic interactions involved in synaptic activities. These attractive features hold great potential for construction of energy-efficient artificial neural networks. However, the memristors made from pristine 2D materials typically exhibit a small dynamic range and poor linearity of switching characteristics. The neural network simulated in the basis of such switching characteristics has a poor learning accuracy of ~43%. In this work, we find that Ar plasma treatment can greatly improve both the dynamic range and linearity of analogue switching characteristics of few-layer MoS2 memristors. The neural network consisting of such plasma-treated memristors is simulated to be able to result in a significantly improved learning accuracy of 94.3% for the MNIST handwritten digits dataset. Our additional Auger electron analysis in combination with electronic characterizations indicates that Ar plasma enhances the concentration of movable S vacancies in MoS2 channels, which leads to improvements in the analogue switching properties. Especially, the average dynamic range of MoS2 memristors are increased from 1.8 to 14.8 after plasma treatment. This work provides scientific insights for controlling the switching characteristics of 2D memristors and provides technical instruction for construction of practical neural networks based on 2D materials.