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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
Summary
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.
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.
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