Robust Ag/ZrO₂/WS₂/Pt Memristor for Neuromorphic Computing

TLDR

Resistive random access memory (RRAM) is a critical nanoscale memristor technology, yet its reliability is limited by the random formation of conductive filaments. This study introduces an Ag/ZrO₂/WS₂/Pt memristor with a 2D WS₂ nanosheet layer, forming a double‑layer oxide–2D material device to enhance reliability over single‑layer structures. The bilayer design exploits differing ion transport rates to confine filament rupture and rejuvenation to the interface, while the device is evaluated on the MNIST handwritten‑digit dataset for neuromorphic simulations. The resulting electrochemical metallization cell shows highly stable switching, tightly clustered ON‑ and OFF‑state voltages, ~10 ns switching speed, endurance exceeding 10⁹ cycles, and successfully implements biosynaptic functions such as spike‑timing‑dependent plasticity and paired‑pulse facilitation, demonstrating its potential for artificial synapses.

Abstract

The development of the information age has made resistive random access memory (RRAM) a critical nanoscale memristor device (MD). However, due to the randomness of the area formed by the conductive filaments (CFs), the RRAM MD still suffers from a problem of insufficient reliability. In this study, the memristor of Ag/ZrO2/WS2/Pt structure is proposed for the first time, and a layer of two-dimensional (2D) WS2 nanosheets was inserted into the MD to form 2D material and oxide double-layer MD (2DOMD) to improve the reliability of single-layer devices. The results indicate that the electrochemical metallization memory cell exhibits a highly stable memristive switching and concentrated ON- and OFF-state voltage distribution, high speed (∼10 ns), and robust endurance (>109 cycles). This result is superior to MDs with a single-layer ZrO2 or WS2 film because two layers have different ion transport rates, thereby limiting the rupture/rejuvenation of CFs to the bilayer interface region, which can greatly reduce the randomness of CFs in MDs. Moreover, we used the handwritten recognition dataset (i.e., the Modified National Institute of Standards and Technology (MNIST) database) for neuromorphic simulations. Furthermore, biosynaptic functions and plasticity, including spike-timing-dependent plasticity and paired-pulse facilitation, have been successfully achieved. By incorporating 2D materials and oxides into a double-layer MD, the practical application of RRAM MD can be significantly enhanced to facilitate the development of artificial synapses for brain-enhanced computing systems in the future.