Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance

TL;DR

This study uses machine learning-enhanced molecular dynamics to investigate hydrogen diffusion in amorphous and crystalline IGZO, linking atomic-scale diffusion mechanisms to device reliability and performance in oxide thin-film transistors.

Contribution

It introduces phase-specific machine learning interatomic potentials for accurate hydrogen diffusion simulation in IGZO, revealing diffusion behaviors relevant to device stability.

Findings

Hydrogen diffuses faster in amorphous IGZO above 750 K due to the glassy matrix.

Hydrogen can reach the device interface within 10^4 seconds at 300-400 K in amorphous IGZO.

Vertical hydrogen diffusion in crystalline IGZO is limited by high energy barriers, reducing impact on device instability.

Abstract

Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulation and bias instability. In this work, we investigate hydrogen diffusion in amorphous IGZO ($a$-IGZO) and $c$-axis aligned crystalline IGZO (CAAC-IGZO) using machine learning interatomic potential molecular dynamics (MLIP-MD) simulations. We construct accurate phase-specific MLIPs by fine-tuning SevenNet-0, a universal pretrained MLIP, and validate the models against a comprehensive dataset covering hydrogen-related configurations and diffusion environments. Hydrogen diffusivity is evaluated over 650--1700 K, revealing enhanced mobility above 750 K in $a$-IGZO due to the glassy matrix, while diffusion at lower temperatures is constrained by the rigid network. Arrhenius extrapolation of the diffusivity indicates that hydrogen in $a$-IGZO can reach the channel/insulator interface within $10^{4}$ seconds at 300--400 K, likely contributing to negative bias stress-induced device degradation. Trajectory analysis reveals that long-range diffusion in $a$-IGZO is enabled by a combination of hydrogen hopping and flipping mechanisms. In CAAC-IGZO, hydrogen exhibits high in-plane diffusivity but severely restricted out-of-plane transport due to a high energy barrier along the $c$-axis. This limited vertical diffusion in CAAC-IGZO suggests minimal impact on bias instability. This work bridges the atomic-level hydrogen transport mechanism and device-level performance in oxide TFTs by leveraging large-scale MLIP-MD simulations.