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Related Experiment Video

Updated: Aug 22, 2025

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A MoS2 Hafnium Oxide Based Ferroelectric Encoder for Temporal-Efficient Spiking Neural Network.

Yu-Chieh Chien1, Heng Xiang1, Yufei Shi1

  • 1Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.

Advanced Materials (Deerfield Beach, Fla.)
|November 11, 2022
PubMed
Summary

A new molybdenum disulfide and hafnium oxide ferroelectric encoder efficiently converts stimuli into spikes for Spiking Neural Networks (SNNs). This biomimetic encoder achieves high accuracy and noise resilience, reducing computational load.

Keywords:
2D materialsferroelectric encoderhafnium zirconium oxidespiking neural networkstime-to-first-spike encoding scheme

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Spiking Neural Networks (SNNs) offer energy-efficient computation for data-intensive AI.
  • Artificial neural encoders are crucial for converting external stimuli into spike-based formats for SNNs.

Purpose of the Study:

  • To demonstrate a molybdenum disulfide (MoS2) and hafnium oxide-based ferroelectric encoder for efficient information processing in SNNs.
  • To leverage the fast domain switching of polycrystalline hafnium oxide for biomimetic spike encoding.

Main Methods:

  • Fabrication of a MoS2/hafnium oxide-based ferroelectric encoder.
  • Exploitation of ferroelectric domain switching for spike generation.
  • Simulation of an SNN using the developed encoder with the MNIST dataset.

Main Results:

  • Achieved a high-performance ferroelectric encoder with superior switching efficiency and a broad dynamic range.
  • Demonstrated an average SNN inference accuracy of 95.14% on the MNIST dataset.
  • Showcased robust noise resilience with 94.73% accuracy under Gaussian noise injection.

Conclusions:

  • The ferroelectric encoder enables temporal-efficient information processing in SNNs.
  • The encoder's performance and noise resilience show practical promise for reducing neural network computational load.
  • This biomimetic approach advances energy-efficient AI hardware.