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.
Zhang et al. (Tue,) studied this question.