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High-Performance Artificial Synapse Device Based on Cs3Bi2Br9/NiO Heterostructure for Bio-Inspired Neuromorphic

Xiuqing Cao1,2, Wenfei Li1, Qingqing Zheng1

  • 1School of Physics and Electronic Information, Guangxi Minzu University, Nanning 530006, People's Republic of China.

ACS Applied Materials & Interfaces
|October 25, 2025
PubMed
Summary

Researchers developed a lead-free perovskite memristor for neuromorphic computing. This biocompatible artificial synapse shows robust synaptic function and high accuracy in handwritten digit recognition, paving the way for sustainable electronics.

Keywords:
Cs3Bi2Br9/NiO heterostructureartificial synapsesion migrationlead-free perovskitesneuromorphic computing

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Neuromorphic computing requires energy-efficient, biocompatible artificial synapses.
  • Lead-based perovskites, while effective, pose environmental concerns.
  • Bismuth-based perovskites offer a promising, eco-friendly alternative.

Purpose of the Study:

  • To develop a lead-free artificial synapse for neuromorphic applications.
  • To investigate the synaptic behavior of a Cs3Bi2Br9/NiO heterostructure memristor.
  • To assess the performance and stability of the developed memristor.

Main Methods:

  • Fabrication of a Cs3Bi2Br9/NiO heterostructure memristor.
  • Characterization of the memristor's resistance switching properties.
  • Evaluation of synaptic functions and long-term stability.
  • Testing performance on the MNIST handwritten digit recognition task.

Main Results:

  • The Cs3Bi2Br9/NiO memristor demonstrated robust synaptic function and stability.
  • Achieved a resistance switching change rate below 7.37% and 60-day ambient stability.
  • Exhibited a memory retention time >7 × 10^3 s and >100 cycles durability.
  • Attained 95.46% accuracy in MNIST recognition, outperforming traditional analog networks.

Conclusions:

  • Lead-free Cs3Bi2Br9/NiO heterostructures are viable for sustainable neuromorphic hardware.
  • The engineered heterostructure enhances device stability and synaptic mimicry.
  • This work offers a scalable pathway for biocompatible electronics and advanced AI.