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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
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Ferroelectric/Antiferroelectric HfZrOx Artificial Synapses/Neurons for Convolutional Neural Network-Spiking Neural
Jinhao Zhang1,2, Kangli Xu3, Lin Lu1,2
1School of Integrated Circuits, Shandong University, Jinan 250100, China.
Nano Letters
|August 19, 2025
Summary
Ferroelectric and antiferroelectric hafnium zirconate devices enable efficient neuromorphic computing. A hybrid deep learning framework using these artificial neurons and synapses achieved superior cardiac MRI classification accuracy.
Area of Science:
- Neuromorphic computing
- Materials science
- Artificial intelligence
Background:
- Brain-inspired neuromorphic computing promises efficient, adaptive platforms.
- Ferroelectric and antiferroelectric hafnium zirconate (HfZrOx) devices show promise for artificial synapse and neuron applications.
- Unique polarization switching characteristics are key to their function.
Purpose of the Study:
- To engineer ferroelectric/antiferroelectric HfZrOx devices for artificial synapse/neuron functions.
- To construct a hybrid convolutional neural network (CNN) and spiking neural network (SNN) framework.
- To evaluate the framework's performance in energy-efficient cardiac magnetic resonance imaging (MRI) classification.
Main Methods:
- Element doping engineering of HfZrOx materials.
- Fabrication of ferroelectric and antiferroelectric devices.
- Implementation of integrate-and-fire behaviors for neuromorphic computing.
- Development of a hybrid CNN-SNN framework using HfZrOx devices.
Main Results:
- HfZrOx-based devices demonstrated excellent endurance (>1 × 109 cycles).
- The hybrid CNN-SNN framework achieved 92.7% accuracy in cardiac MRI classification, outperforming pure CNN (82.3%).
- The framework successfully integrated ferroelectric/antiferroelectric properties for neuromorphic functions.
Conclusions:
- Engineered HfZrOx ferroelectric and antiferroelectric materials can realize artificial synapse/neuron functions.
- A complementary HfZrOx-based hybrid CNN-SNN framework enables efficient neuromorphic computing.
- This approach enhances 3D image recognition accuracy and energy efficiency, demonstrating potential for advanced AI applications.
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