Ferroelectric Charged Domain-Wall Synapse for Neuromorphic Computing.

Journal: Nano letters
Published Date: Jul 24, 2025

Abstract

Inspired by brain neural networks, integrated memory-computing devices are critical to meet the demands of big data and artificial intelligence. This work explores the quasi-continuous modulation of ferroelectric charged domain walls' conductance, which is confined in a topological quad-domain, allowing the charged domain walls to serve as neural synapses. The device mimics synaptic plasticity (long-term potentiation and depression) and shows paired impulse facilitation. In a designed ferroelectric domain-wall neural network, we demonstrate multiplicative, accumulation-additive operations between the input image and the stored response matrix, capable of image processing functions, including triclassification with 100% accuracy. In the neural network simulation, the MINST database and the Cifar-10 database achieve 98.7% and 95.1% recognition rates. The sub-nanosecond polarization switching and the ultrathin (3-5 nm) charged domain walls position them as a promising platform for advancing ultrafast and scalable synaptic devices for low-power (potentially reduced to 0.2 aJ with sub-nanosecond pulse durations) neuromorphic computing systems.

Authors

  • Chen Liang
    Shanghai Institute of Forensic Science, Shanghai Key Laboratory of Crime Scene Evidence, Shanghai 200083, China.
  • Ye Wang
    College of Computer Science and Technology, Jilin University, Changchun 130012, China.
  • Yiming Liu
  • Xiaoming Shi
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, People's Republic of China.
  • Ji Ma
    UCLA Henry Samueli School of Engineering and Applied Science, Los Angeles, USA.
  • Wael Ben Taazayet
    Advanced Research Institute of Multidisciplinary Sciences, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • QingHua Liang
    School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China. qhliang@sjtu.edu.cn.
  • Huayu Yang
    Department of Liver Surgery, PUMCH, PUMC & CAMS, Beijing, 100730, China.
  • Yuanyuan Fan
    Advanced Research Institute of Multidisciplinary Sciences, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • Jiafang Li
    School of Physics, Beijing Institute of Technology, Beijing 100081, China.
  • Congli He
    School of Physics and Astronomy, Beijing Normal University, Beijing 100875, China.
  • Ying Fu
    Department of Ultrasound, Peking University Third Hospital, Beijing, China.
  • Houbing Huang
    Advanced Research Institute of Multidisciplinary Sciences, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Ce-Wen Nan
    State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China.

Keywords

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