AI and Physics-Based Computational Methods for Oxide Thin-Film Transistors: A Review

Oxide thin-film transistors (TFTs) are critical components in modern display technologies due to their high mobility, optical transparency, and low-temperature processability. As the design space expands across material systems, device architectures, and operating conditions, there is a growing demand for computational methods that support reliable and efficient modeling. This review presents a comprehensive overview of AI- and physics-based methods for oxide TFTs, spanning from atomistic material analysis to circuit-level modeling. We discuss atomistic simulations such as density functional theory (DFT) and molecular dynamics (MD) for defect energetics and carrier behavior, technology computer-aided design (TCAD) for device-level electrothermal analysis, and compact models for circuit simulation. The role of artificial intelligence in surrogate modeling, parameter extraction, optimization of materials, device structures, and processes is discussed. By bridging simulation methods across multiple scales, this review provides insights into accelerating the design, and analysis of oxide TFTs.