Journal of Materiomics

Journal of Materiomics

Volume 8, Issue 1, January 2022, Pages 144-149
Journal of Materiomics

A flexible BiFeO3-based ferroelectric tunnel junction memristor for neuromorphic computing

https://doi.org/10.1016/j.jmat.2021.04.009Get rights and content
Under a Creative Commons license
open access

Highlights

  • The flexible FTJ memristor, with stable multiple resistance states, shows robust resistive switchings in 103 bending cycles.

  • Synaptic functions including spike-timing-dependent plasticity, long-term depression and long-term potentiation are emulated.

  • Accurate conductance manipulations with small nonlinearity (−0.24) and low cycle-to-cycle variation (1.77%) are realized.

  • Experimental results based simulations achieve high recognition accuracies for handwritten digits (92.8%) and images (86.2%).

Abstract

Ferroelectric tunnel junctions (FTJs) as the artificial synaptic devices have been considered promising for constructing brain-inspired neuromorphic computing systems. However, the memristive synapses based on the flexible FTJs have been rarely studied. Here, we report a flexible FTJ memristor grown on a mica substrate, which consists of an ultrathin ferroelectric barrier of BiFeO3, a semiconducting layer of ZnO, and an electrode of SrRuO3. The obtained flexible FTJ memristor exhibits stable voltage-tuned multi-states, and the resistive switchings are robust after 103 bending cycles. The capability of the FTJ as a flexible synaptic device is demonstrated by the functionality of the spike-timing-dependent plasticity with bending, and the accurate conductance manipulation with small nonlinearity (−0.24) and low cycle-to-cycle variation (1.77%) is also realized. Especially, artificial neural network simulations based on experimental device behaviors reveal that the high recognition accuracies up to 92.8% and 86.2% are obtained for handwritten digits and images, respectively, which are close to the performances for ideal memristors. This work highlights the potential applications of FTJ as flexible electronics for data storage and processing.

Keywords

Flexible ferroelectric tunnel junction
Memristor
Artificial synapse
Neuromorphic computing

Cited by (0)

Hao-Yang Sun is a Ph.D. student in Department of Physics, University of Science and Technology of China. His research focuses on physical properties of flexible inorganic functional oxides.

Zhen Luo is a Ph.D. student in Department of Physics, University of Science and Technology of China. His research focuses on ferroelectric tunnel junction memristors and artificial neural networks.

Dr. Yue-Wei Yin is a professor at Department of Physics in University of Science and Technology of China. His current research interests include fundamental and applied aspects of perovskite films and heterostructures and prototype ferroelectric and multiferroic based electronic devices.

Dr. Xiao-Guang Li is a professor of materials physics in Hefei National Laboratory for Physical Sciences at the Microscale and Department of Physics, University of Science and Technology of China. His research interests include the synthesis, microstructure, physical properties, and prototype devices of transitional metal oxides.

Peer review under responsibility of The Chinese Ceramic Society.

1

These authors contributed equally to this work.