High-Performance Synapse Arrays for Neuromorphic Computing via Floating Gate-Engineered IGZO Synaptic Transistors

Abstract

Neuromorphic computing emulating the human brain offers a promising alternative to the Von Neumann architecture. Developing artificial synapses is essential for implementing hardware neuromorphic systems. Indium-gallium-zinc oxide (IGZO)-based synaptic transistors using charge trapping have advantages, such as low-temperature process and complementary metal-oxide-semiconductor compatibility. However, these devices face challenges of low charge de-trapping efficiency and insufficient retention. Here, IGZO synaptic transistors are introduced utilizing an indium-tin oxide (ITO) floating gate (FG) to overcome these limitations. The ITO FG's higher conductivity and alleviated chemical interactions with the Al₂O₃ tunneling layer (TL) deposited by atomic layer deposition result in enhanced electrical performance with a smooth FG/TL interface. An 8 × 8 synapse array achieves 100% yield and successful programming without interference using a half-pulse scheme. Spiking neural network simulations on MNIST and Fashion-MNIST datasets demonstrate high accuracies of 98.31% and 87.76%, respectively, despite considering device variations and retention. These findings highlight the potential of IGZO synaptic transistors for neuromorphic computing applications.