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Ferroelectric Charged Domain-Wall Synapse for Neuromorphic Computing.

Chen Liang1, Ye Wang1, Yiming Liu2

  • 1Advanced Research Institute of Multidisciplinary Sciences, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.

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This study introduces ferroelectric domain walls as artificial synapses for neuromorphic computing. These devices enable efficient, low-power AI by mimicking brain functions for advanced image recognition.

Keywords:
BiFeO3 nanoislandscharged domain wallsferroelectric topological domainsimage recognitionneuromorphic synapse

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Integrated memory-computing devices are essential for big data and artificial intelligence.
  • Brain-inspired neural networks offer a paradigm for efficient computation.

Purpose of the Study:

  • To explore the use of ferroelectric charged domain walls as artificial synapses.
  • To demonstrate the potential of these devices in neuromorphic computing systems.

Main Methods:

  • Quasi-continuous modulation of ferroelectric charged domain walls' conductance.
  • Fabrication of a ferroelectric domain-wall neural network.
  • Testing synaptic plasticity (long-term potentiation and depression) and paired impulse facilitation.
  • Demonstration of multiplicative, accumulation-additive operations for image processing.

Main Results:

  • Ferroelectric domain walls successfully mimicked synaptic plasticity and paired impulse facilitation.
  • The designed neural network achieved 100% accuracy in triclassification tasks.
  • High recognition rates of 98.7% (MINST) and 95.1% (Cifar-10) were achieved in simulations.
  • Demonstrated potential for ultrafast (sub-nanosecond switching) and low-power (0.2 aJ) operation.

Conclusions:

  • Ferroelectric charged domain walls are a promising platform for scalable synaptic devices.
  • This technology advances ultrafast, low-power neuromorphic computing systems.
  • The developed devices show significant potential for AI applications, particularly in image recognition.