A volatile nanocomposite memristor with a phase stratification dielectric layer: threshold switching with rich neuromorphic dynamics

Abstract

Volatile threshold memristive switches are attractive for the fabrication of artificial compact neurons, third-order neuromorphic elements, and reservoir computing (RC) systems. The latter have emerged as a powerful neuromorphic framework for efficient temporal signal processing, offering a compelling combination of computational efficiency and low training cost. A key challenge, however, lies in finding hardware platforms that possess the necessary short-term memory and nonlinear dynamics to serve as physical reservoirs. In this work, we address this by introducing a volatile bi-layer nanocomposite-based (Co–Fe–B)–LiNbO_x/(NbO_y)–LiNbO_z memristor, which exhibits pronounced and batch-to-batch reproducible short-term memory properties. A distinctive feature of this device is the cooperative nature of its threshold and resistive switching, allowing it to be modeled as a parallel connection of an NbO₂-based selector and a LiNbO₃-based nonvolatile memristor. We demonstrate that even a compact hardware system built around a single memristor can perform complex tasks. Specifically, we achieved 97.1% accuracy in handwritten-digit recognition from the MNIST dataset and a low normalized root mean square error of 0.028 in predicting the chaotic Hénon map time-series. This study expands the range of promising volatile nanocomposite memristive structures for neuromorphic computing applications, while additionally paving the way toward scalable, high-performance memristor-based RC systems for efficient complex temporal and spatial information processing.