标题： 原子尺度关于机器学习原子间势中氢迁移的见解：将原子扩散与器件性能联系起来 显示英文标题

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

摘要： 理解氢扩散对于提高氧化物薄膜晶体管（TFT）的可靠性和性能至关重要，其中氢在载流子调制和偏置不稳定性中起关键作用。 在本工作中，我们使用机器学习原子间势分子动力学（MLIP-MD）模拟研究非晶IGZO（$a$-IGZO）和$c$轴对齐结晶IGZO（CAAC-IGZO）中的氢扩散。 我们通过微调通用预训练的MLIP SevenNet-0，构建了精确的相特异性MLIP，并将模型与涵盖氢相关构型和扩散环境的全面数据集进行验证。 氢扩散率在650–1700 K范围内评估，结果显示在$a$-IGZO中，由于玻璃基质，在750 K以上扩散率增强，而较低温度下的扩散则受刚性网络限制。 扩散率的阿伦尼乌斯外推表明，在300–400 K下，氢在$a$-IGZO中可在$10^{4}$秒内到达沟道/绝缘体界面，这可能有助于负偏压应力引起的器件退化。 轨迹分析显示，$a$-IGZO中的长程扩散是由氢跳跃和翻转机制共同实现的。 在CAAC-IGZO中，氢表现出高的平面内扩散率，但由于沿$c$轴的高能垒，其面外传输受到严重限制。 CAAC-IGZO中有限的垂直扩散表明对偏置不稳定性影响最小。 本工作通过利用大规模MLIP-MD模拟，将原子级的氢传输机制与器件级性能联系起来。 显示英文摘要

摘要： 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.