What type of study is this?

This is a In Vitro Study study.

August 19, 2025

Ferroelectric/Antiferroelectric HfZrOx Artificial Synapses/Neurons for Convolutional Neural Network–Spiking Neural Network Neuromorphic Computing

Key Points

HfZrOx devices demonstrate enhanced accuracy in 3D image recognition, improving from 82.3% to 92.7%.
The devices exhibit remarkable endurance of 1 × 10^9 cycles, essential for long-term applications.
Convolutional and spiking neural networks leverage polarization switching for efficient computations in neuromorphic designs.
This hybrid system suggests potential breakthroughs in energy-efficient classification methods for MRI imaging.

Abstract

Brain-inspired neuromorphic computing offers significant potential for efficient and adaptive computational platforms. Emerging ferroelectric and antiferroelectric HfZrOx devices provide key roles in convolutional neural network (CNN) and spiking neural network (SNN) computing with unique polarization switching characteristics. Here, we present ferroelectric/antiferroelectric HfZrOx devices to realize functions of artificial synapse/neurons by element doping engineering. The HfZrOx-based ferroelectric and antiferroelectric devices exhibit excellent endurance characteristics of 1 × 109 cycles. Based on the non-volatile polarization switching and spontaneous depolarization nature of ferroelectric and antiferroelectric devices, integrate-and-fire behaviors were constructed for neuromorphic computing. For the first time, a complementary ferroelectric/antiferroelectric HfZrOx artificial synapse/neuron-based hybrid CNN-SNN framework was constructed for energy-efficient cardiac magnetic resonance imaging (MRI) classification. The hybrid neural network breaks the limitation of pure SNN in 3D image recognition and improves the accuracy from 82.3 to 92.7% compared to pure CNN, highlighting the potential of composition-engineered ferroelectric materials to implement high-efficiency neuromorphic computing.

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