Ferroelectric Aluminum Scandium Nitride Transistors with Intrinsic Switching Characteristics and Artificial Synaptic Functions for Neuromorphic Computing
Jing Gao, Yu‐Chieh Chien, Lingqi Li, Hock Koon Lee, Subhranu Samanta, Binni Varghese, Heng Xiang, Minghua Li, Chen Liu, Yao Zhu, Li Chen, Kah‐Wee Ang
Abstract
Abstract Aluminum Scandium Nitride (Al 1−x Sc x N) has received attention for its exceptional ferroelectric properties, whereas the fundamental mechanism determining its dynamic response and reliability remains elusive. In this work, an unreported nucleation‐based polarization switching mechanism in Al 0.7 Sc 0.3 N (AlScN) is unveiled, driven by its intrinsic ferroelectricity rooted in the ionic displacement. Fast polarization switching, characterized by a remarkably low characteristic time of 0.00183 ps, is captured, and effectively simulated using a nucleation‐limited switching (NLS) model, where the profound effect of defects on the nucleation and domain propagation is systematically studied. These findings are further integrated into Monte Carlo simulations to unravel the influence of the activation energy for ferroelectric switching on the distributions of switching thresholds. The long‐term reliability of devices is also confirmed by time‐dependent dielectric breakdown (TDDB) measurements, and the effect of thickness scaling is discussed. Ferroelectric field‐effect transistors (FeFETs) are demonstrated through the integration of AlScN and 2D MoS 2 channel, where biological synaptic functions can be emulated with optimized operation voltage. The artificial neural network built from AlScN‐based FeFETs achieves 93.8% recognition accuracy of handwritten digits, demonstrating the potential of ferroelectric AlScN in future neuromorphic computing applications.