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arXiv 2405.13635cond-mat.mtrl-sci
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用于硅热氧化的机器学习力场

Machine Learning Force Field for Thermal Oxidation of Silicon

Lukas Cvitkovich, Franz Fehringer, Christoph Wilhelmer, Diego Milardovich, Dominic Waldhör, Tibor Grasser

AI总结:

本文针对 Si/SiO$_2$ 界面原子模拟需求,利用密度泛函理论数据训练机器学习力场,以兼顾经典力场效率与从头算精度,并显著优于 reaxFF。

摘要:

回顾其在半导体工业中长达七十年的广泛应用,硅及其原生氧化物 SiO$_2$ 仍处于若干技术发展的核心。近年来,超薄氧化层的制备已成为跟上纳米电子器件缩小化趋势以及实现新型器件技术的关键。随之而来的是更好理解 Si/SiO$_2$ 界面原子构型的需求。经典力场应用灵活且计算成本相对较低,但精度有限。从头算方法可提供好得多的结果,却极其昂贵。机器学习力场(MLFF)提供了结合两者优势的可能性。我们训练了一个 MLFF,用于模拟 Si 衬底的干热氧化过程。训练数据由密度泛函理论计算生成。所得结构与从头算模拟以及实验观测均相符。与经典力场、即最新的反应力场(reaxFF)相比,所得构型得到了大幅改善。

英文摘要:

Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO$_2$ are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO$_2$ interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab-initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data is generated by density functional theory calculations. The obtained structures are in line with ab-initio simulations as well as with experimental observations. Compared to a classical force field, the most recent reactive force field (reaxFF), the resulting configurations are vastly improved.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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