Ferroelectric/Antiferroelectric HfZrO<sub><i>x</i></sub> Artificial Synapses/Neurons for Convolutional Neural Network–Spiking Neural Network Neuromorphic Computing
Jinhao Zhang, Kangli Xu, Lin Lü, Lu Chen, Xinchen Tao, Yongkai Liu, Jiajie Yu, Jialin Meng, David Wei Zhang, Tianyu Wang, Lin Chen
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.