Ferroelectric/Antiferroelectric HfZrO_<i>x</i> Artificial Synapses/Neurons for Convolutional Neural Network–Spiking Neural Network Neuromorphic Computing

Abstract

Brain-inspired neuromorphic computing offers significant potential for efficient and adaptive computational platforms. Emerging ferroelectric and antiferroelectric HfZrO x 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 HfZrO x devices to realize functions of artificial synapse/neurons by element doping engineering. The HfZrO x -based ferroelectric and antiferroelectric devices exhibit excellent endurance characteristics of 1 × 10 9 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 HfZrO x 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.