Realization of Brain-Inspired Devices Using Giant Resistive Switching in Ferroelectric Oxides: Mimicking Synaptic Functions through Electric Polarization of Ferroelectrics

Conventional electronics have reached the limits of performance improvement through miniaturization, and power efficiency is no longer sufficient to meet the demands of the AI and big data era. As an alternative, neuromorphic computing, which mimics the highly efficient information processing of the human brain, has attracted worldwide attention. Among its core devices, memristors are promising because they can change and retain resistance according to input history, thereby reproducing synaptic plasticity. However, existing devices face issues in structure, stability, and power consumption, preventing practical use. The research group focused on a new memristor using ferroelectrics, introducing oxygen vacancies into PbTiO₃ thin films with large spontaneous polarization. This enabled semiconducting conduction and resistance switching, achieving dramatic changes in resistance at low voltages and a world-leading ON/OFF ratio of 10⁵ for single-layer films. The device also demonstrated remarkable stability, unaffected by environmental changes and showing no degradation even after over 100 voltage sweeps. Synapse-mimicking experiments confirmed spike-timing-dependent resistance changes, reproducing brain-like learning functions. Furthermore, image recognition tests using backpropagation and convolutional neural networks achieved high accuracy exceeding 92%. These results demonstrate the potential of ferroelectric thin-film memristors and open the way for the development of next-generation high-performance neuromorphic devices.

Figure 1: (a) Cross-sectional transmission electron microscopy (TEM) image of the PbTiO₃ thin film. The PbTiO₃ layer was grown on a single-crystal oxide substrate composed of niobium (Nb), strontium (Sr), and titanium (Ti). (b) The ON/OFF ratios of resistive switching in ferroelectric memristors. (c) Learning and forgetting characteristics of electrical information in the PbTiO₃ memristor.

Papers

Journal: Advanced Functional Materials

Title: Enhanced Switching Performance in Single-Crystalline PbTiO₃ Ferroelectric Memristors for Replicating Synaptic Plasticity

Authors: Haining Li, Zhiqiang Liao, Risa Kataoka, Md Sarker Shamim, Takeshi Kijima, Hiroyasu Yamahara, Hitoshi Tabata, and Munetoshi Seki*

DOI: 10.1002/adfm.202510715