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Related Experiment Video

Updated: Jan 17, 2026

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Additive-Engineered CsPbBr3-Based Perovskite Memristors for Neuromorphic Computing and Associative Learning

Zhiqiang Xie1, Jianchang Wu1,2, Jingjing Tian1

  • 1Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Martensstraße 7, Erlangen 91058, Germany.

ACS Applied Materials & Interfaces
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel perovskite memristor using a carbohydrazide additive to overcome fabrication challenges. The enhanced device mimics brain functions and achieves high accuracy in image classification for neuromorphic computing.

Keywords:
additive engineeringartificial synapsesassociative learningnonvolatileperovskite memristor

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Perovskite memristors show promise for neuromorphic computing due to their properties.
  • All-inorganic CsPbBr3 perovskites offer excellent stability but face fabrication challenges.
  • Low solubility of CsBr hinders the creation of high-quality CsPbBr3 films.

Purpose of the Study:

  • To develop a high-performance CsPbBr3-based memristor for neuromorphic applications.
  • To address the solubility issue of CsBr in CsPbBr3 film fabrication.
  • To demonstrate the potential of additive engineering in enhancing memristor performance.

Main Methods:

  • A facile one-step spin-coating approach was used.
  • A carbohydrazide (CBH) additive was incorporated into the perovskite precursor.
  • The fabricated memristors were tested for synaptic functions and in a convolutional neural network.

Main Results:

  • The modified memristor exhibited improved ON/OFF ratio, endurance, and retention time.
  • The device successfully emulated synaptic functions like EPSC, PPF, LTP/LTD, and learning-forgetting behaviors.
  • An associative learning experiment demonstrated memory formation and extinction.
  • High recognition accuracy (89.07%) was achieved in Fashion MNIST classification using a CNN.

Conclusions:

  • Additive engineering with CBH is an effective strategy for high-performance perovskite memristors.
  • The developed CsPbBr3 memristor shows significant potential for neuromorphic computing.
  • This work paves the way for advanced memory devices mimicking biological synapses.