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Updated: Oct 3, 2025

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Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy-Efficient Image

Sanghyeon Choi1, Jingon Jang1, Min Seob Kim2

  • 1KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|February 16, 2022
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Summary

Researchers developed a novel silicon oxide memristive synaptic device inspired by the human brain. This energy-efficient computing framework significantly reduces learning energy while maintaining high accuracy for pattern recognition tasks.

Keywords:
barristordrop-connected networkneuromorphic computingprobabilistic synapsesiliconsilicon oxide

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Human neural networks utilize sparse, probabilistic synapses for energy-efficient cognition.
  • Current computing architectures lack the efficiency of biological systems.

Purpose of the Study:

  • To design and fabricate a flexible neural network with probabilistic synapses.
  • To mimic brain-inspired synaptic plasticity for energy-efficient computing.

Main Methods:

  • Fabrication of a 16x16 crossbar array using gate-tunable probabilistic SiOx memristive synaptic barristors.
  • Utilizing Si/graphene heterojunctions for controllable stochastic switching dynamics.
  • Implementing electrostatic gating for threshold tunability of probabilistic switching activation (PAct).

Main Results:

  • Achieved in situ alteration of PAct from 0 to 1.0 via electrostatic gating.
  • Developed a drop-connected algorithm for shape classification of fashion items.
  • Demonstrated a ≈2,116-fold reduction in learning energy compared to conventional networks.
  • Attained a high recognition accuracy of ≈93%.

Conclusions:

  • The developed memristive synaptic device offers a pathway towards ultimate energy-efficient computing.
  • Threshold-tunable probabilistic synapses are crucial for efficient artificial intelligence.
  • This approach significantly enhances learning efficiency and accuracy in neuromorphic systems.