MoS2-based Quantum Dot Artificial Synapses for Neuromorphic Computing

Abstract

The advancement of deep learning has escalated computational requirements. Neuromorphic devices, particularly those based on memristors, present strong potential to meet these demands. However, current memristors face challenges such as a low on/off ratio and poor linearity, which hinder the progress of neuromorphic computing. Here, we propose a MoS₂-based quantum dot memristor, where the presence of quantum dots facilitates the formation and stability of conductive channels. The device exhibits narrow set and reset voltage distributions, with an on/off ratio reaching 10⁵ and multiple resistive states. Based on these multi-state characteristics, we achieved parallel image processing with various operators. The excitatory postsynaptic current (EPSC), spike-timing-dependent plasticity (STDP), paired-pulse facilitation (PPF), long-term potentiation (LTP), and long-term depression (LTD) characteristics of the device were tested, with the linearity of LTP and LTD being 0.21 and -0.25, respectively. Based on the good linearity of weight updates, we built an artificial neural network to recognize facial images with Gaussian, salt-and-pepper, and Poisson noise. At noise levels of 40%, 48%, and λ = 80, the recognition accuracy rates were still as high as 100%, 100%, and 97.33%, respectively. This work provides a valuable reference for quantum dot-based neuromorphic computing.