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Current/Voltage Dual-Modal Hybrid Ionotronic Oxide Dendrite Transistor for Neuromorphic Computing.

Wei Sheng Wang1,2, Xin Huang1, You Jie Huang1

  • 1School of Physical Science and Technology, Ningbo University, Ningbo 315211, Zhejiang, P. R. China.

ACS Applied Materials & Interfaces
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

A novel hybrid ionotronic oxide dendrite transistor (HIODT) enables dual-modal current/voltage control for neuromorphic computing. This device achieves high accuracy in pattern recognition and emulates biological pain perception, advancing artificial intelligence hardware.

Keywords:
current/voltage dual-modal cognitionhybrid ionotronic oxide dendrite transistorinterfacial protonic/electronic couplingneuromorphic computingpain perceptual nociceptors

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Voltage-driven neuromorphic devices address the von Neumann bottleneck, but current-driven approaches face challenges in implementing synaptic functions.
  • Efficient neuromorphic computing requires devices that can mimic complex neural behaviors and learning.
  • Mimicking biological sensory systems, like pain perception, in artificial devices offers new avenues for advanced AI.

Purpose of the Study:

  • To propose and characterize a current/voltage dual-modal hybrid ionotronic oxide dendrite transistor (HIODT).
  • To demonstrate the HIODT's capability in performing basic synaptic functions and learning behaviors.
  • To explore the device's potential for pattern recognition and emulating biological sensory functions.

Main Methods:

  • Fabrication and electrical characterization of the HIODT.
  • Implementation of current and voltage spike schemes for synaptic weight updating and associative learning.
  • Testing the device's performance in a three-layer artificial neural network for digit and Fashion-MNIST recognition.
  • Emulation of pain perceptual nociceptor (PPN) behaviors like sensitization and desensitization.

Main Results:

  • The HIODT demonstrated good electrical performance and rich ion dynamics.
  • Effective linear synaptic weight updating and associative learning were achieved using dual-modal modulation.
  • Recognition accuracies of >90% for small digits and ~80% for Fashion-MNIST were obtained.
  • Key features of pain perceptual nociceptors were successfully emulated.

Conclusions:

  • The proposed HIODT offers a promising platform for current/voltage dual-modal neuromorphic computing.
  • The device's ability to perform complex learning and emulate biological functions opens new possibilities for advanced AI.
  • This work provides valuable insights into dual-modal spiking strategies for next-generation functional neuromorphic devices.