High‐Performance Synapse Arrays for Neuromorphic Computing via Floating Gate‐Engineered IGZO Synaptic Transistors
Junhyeong Park, Yumin Yun, Sunyeol Bae, Yuseong Jang, Seungyoon Shin, Soo‐Yeon Lee
Abstract
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 Al 2 O 3 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.