用机器学习加速半导体器件模拟，揭示氧化物界面电子行为
Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures
- 结合机器学习电子结构与量子输运求解器，实现超快模拟
- 可处理两万原子以上器件，速度比传统方法快一万倍
- 发现金属原子未配位态显著影响电流大小与传输
我们提出一种新型从头算方法，将可扩展的机器学习（ML）电子结构模型与先进的量子输运（QT）求解器相结合，用于模拟半导体器件。该框架在保持高预测精度的前提下，使包含超过20,000个原子的器件哈密顿矩阵生成速度相比密度泛函理论（DFT）提升10,000倍。利用其独特能力，我们研究了MoS2/氧化物样品及单层MoS2场效应晶体管，其中氧化物层（如HfO2或Al2O3）被显式纳入量子输运域。结果表明，靠近半导体-氧化物界面的未配位金属原子（Hf或Al）显著影响电子电流的大小及其在MoS2中的传播。
原文摘要 · Abstract (English)
We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport (QT) solver. The developed framework enables 10,000X speedups over density functional theory to produce the Hamiltonian matrix of devices made of >20,000 atoms, while offering high prediction accuracy. We use its unique features to investigate MoS2/oxide samples and single-layer MoS2 field-effect transistors, where the surrounding oxide layers, here, HfO2 or Al2O3, are explicitly included into the QT domain. In particular, we reveal that the presence of undercoordinated metal atoms (Hf or Al) close to the semiconductor-oxide interface significantly affects the magnitude of the electronic current and its propagation through MoS2.
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