Monolayer molybdenum disulfide has been previously discovered to exhibit non-volatile resistive switching behavior in a vertical metal-insulator-metal structure, featuring ultra-thin sub-nanometer active layer thickness. However, the reliability of these nascent 2D-based memory devices was not previously investigated for practical applications. Here, we employ an electron irradiation treatment on monolayer MoS₂ film to modify the defect properties. Raman, photoluminescence, and X-ray photoelectron spectroscopy measurements have been performed to confirm the increasing amount of sulfur vacancies introduced by the e-beam irradiation process. The statistical electrical studies reveal the reliability can be improved by up to 1.5× for yield and 11× for average DC cycling endurance in the devices with a moderate radiation dose compared to unirradiated devices. Based on our previously proposed virtual conductive-point model with the metal ion substitution into sulfur vacancy, Monte Carlo simulations have been performed to illustrate the irradiation effect on device reliability, elucidating a clustering failure mechanism. This work provides an approach by electron irradiation to enhance the reliability of 2D memory devices and inspires further research in defect engineering to precisely control the switching properties for a wide range of applications from memory computing to radio-frequency switches.

In this article, the influence of the temperature instability of resistive memory switching on potential neuromorphic computing applications is extensively studied using an Intel TaO $_{{\textit{x}}}$ -based analog-type memristor as a synaptic weight modulator in a neural network. Evaluation results show that the effect of ambient temperature during training and interference can degrade the neural network’s accuracy due to inefficient weight updates and inevitable resistance or conductance drifting. Our results provide additional insights into device-level physical models and simple circuit-level design guidance for potential hardware-based neuromorphic computing applications.

Constant voltage and current stress were applied on MoS ₂ resistive switching devices, showing unique behaviors explained by a modified conductive-bridge-like model.

The 2-D materials have become promising candidates for resistive random access memory (RRAM) devices as more unique resistive switching (RS) characteristics have recently been revealed. However, endurance is a major challenge for industrialization. Unlike the well-developed and recognized conductive filament (CF) model for oxide-based RRAM, the RS mechanism for 2-D RRAM is not well understood. In this article, we first review the dissociation–diffusion–adsorption (DDA) model and the cluster model proposed in previous works on monolayer 2-D RRAM devices. The use of a Monte Carlo (MC) simulator for multilayer 2-D RRAM devices to expand the application of DDA and cluster models is then discussed. A simulator was designed to provide an intuitive physical view by visualizing the stochastic behaviors of the RS process in multilayer 2-D RRAM devices. By comparing the simulated results with experimental data, the endurance characteristic was found to be mainly determined by the formation and collapse of an effective switching layer. We also found that the thickness of the effective switching layer is independent of the total thickness of the multilayer 2-D material and the initial status of the device, which is consistent with the experimental observations. The model and results discussed in this work provide additional insights and guidance for improving the reliability of 2-D RRAM devices.