All-Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons

Abstract

Artificial neurons and synapses are crucial for efficiently implementing spiking neural networks (SNNs) in hardware. The distinct functional requirements of artificial neurons and synapses present significant challenges in the implementation of area- and energy-efficient SNNs. This study reports an all-ferroelectric SNN system through co-optimization of material properties and device configurations using wafer-scale atomic layer deposition. For the first time, a double-gate (DG) morphotropic phase boundary-based thin-film transistor (MPBTFT) is utilized for a leaky integrate-and-fire (LIF) neuron. The DG MPBTFT-based LIF neuron eliminates the need for capacitors and reset circuits, thereby enhancing area and energy efficiency. The DG configuration demonstrates various neuronal functions with high reliability. Co-optimizing materials and devices significantly enhance the performance and functional versatility of artificial neurons and synapses. Meticulous material engineering facilitates the seamless co-integration of DG MPBTFT-based neurons, ferroelectric thin-film transistor (TFT)–based synapses, and normal TFTs on a single wafer. All-ferroelectric SNN systems achieved a high classification accuracy of 94.9%, thereby highlighting the potential of DG MPBTFT-based LIF neurons for advanced neuromorphic computing.