Defect-engineered monolayer MoS2 with enhanced memristive and synaptic functionality for neuromorphic computing

Abstract

Two-dimensional transition metal dichalcogenides (TMDs)-based memristors are promising candidates for realizing artificial synapses in next-generation computing. However, practical implementation faces several challenges, such as high non-linearity and asymmetry in synaptic weight updates, limited dynamic range, and cycle-to-cycle variability. Here, utilizing optimal-power argon plasma treatment, we significantly enhance the performance matrix of memristors fabricated from monolayer MoS2. Our approach not only improves linearity and symmetry in synaptic weight updates but also increases the number of available synaptic weight updates and enhances Spike-Time Dependent Plasticity. Notably, it broadens the switching ratio by two orders, minimizes cycle-to-cycle variability, reduces non-linear factors, and achieves an energy consumption of  ~30 fJ per synaptic event. Implementation of these enhancements is demonstrated through Artificial Neural Network simulations, yielding a learning accuracy of  ~97% on the MNIST hand-written digits dataset. Our findings underscore the significance of defect engineering as a powerful tool in advancing the synaptic functionality of memristors. Memristors based on 2D materials are promising candidates for realizing artificial synapses in next-generation computing. Here, utilizing optimal-power argon plasma treatment, the authors enhance the performance of memristors fabricated from monolayer MoS2, reducing non-linearity and asymmetry in synaptic weight updates and minimizing cycle-to-cycle variability.