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Sustainable Synaptic Device with Two-Dimensional Ferroelectric Materials for Neuromorphic Computing.

Jaewook Yoo1, Seokjin Oh2, Minah Park1

  • 1Division of Electronic Engineering, Jeonbuk National University, Jeonju, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|May 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a durable 2D ferroelectric synaptic device using self-curing for neuromorphic computing. This innovation enhances device endurance during repetitive learning, crucial for big-data processing.

Keywords:
2D ferroelectricsconvolution neural networks (CNN)current annealing (CA)sustainable synaptic devicesα‐In2Se3

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Area of Science:

  • Materials Science
  • Neurotechnology
  • Computer Engineering

Background:

  • Neuromorphic computing aims to mimic the brain for big-data challenges.
  • Synaptic devices degrade during in situ training due to plasticity mimicry.

Purpose of the Study:

  • To develop a durable 2D ferroelectric synaptic device for neuromorphic computing.
  • To investigate self-curing mechanisms for enhanced device endurance.

Main Methods:

  • Fabrication of a two-dimensional (2D) ferroelectric α-In2Se3-based synaptic device.
  • Utilizing current annealing (CA) for self-curing during synaptic fatigue.
  • Employing low-frequency noise (LFN) spectroscopy to analyze device mechanisms.

Main Results:

  • The 2D ferroelectric synaptic device demonstrated enhanced durability via self-curing.
  • Low-frequency noise spectroscopy elucidated conduction, degradation, and self-curing mechanisms.
  • Classification accuracy on the CIFAR-10 dataset recovered to initial levels after self-curing.

Conclusions:

  • Self-curing in 2D ferroelectric synaptic devices improves endurance and performance.
  • 2D ferroelectric materials are promising for robust neuromorphic computing applications.
  • Self-curing can reduce long-term energy consumption in synaptic arrays.