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arXiv 2201.00516cond-mat.mtrl-sci
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基于深度势能的砷化硼冷却衬底上氮化镓器件的多尺度模拟

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

Jing Wu, E Zhou, An Huang, Hongbin Zhang, Ming Hu, Guangzhao Qin

AI总结:

本文利用机器学习深度势能对BAs-GaN异质结构进行多尺度模拟,揭示其265 MW m⁻²K⁻¹的超高界面热导及晶粒尺寸与边界热阻的竞争关系,为高效散热电子器件设计提供新途径。

摘要:

高效散热对于高功率密度电子器件至关重要。实验上已经实现了将超高导热率砷化硼(BAs,1300 W m⁻¹K⁻¹)冷却衬底集成到宽禁带半导体氮化镓(GaN)器件中。然而,缺乏对BAs-GaN界面热传递的系统分析阻碍了其实际应用。在本研究中,通过构建准确且高效的机器学习原子间势,我们对BAs-GaN异质结构进行了多尺度模拟。实现了265 MW m⁻²K⁻¹的超高界面热导(ITC),这源于BAs和GaN之间良好匹配的晶格振动。此外,随着尺寸从1 nm增大到100 μm,揭示了晶粒尺寸与边界热阻之间的竞争关系。这种配备深度势能的多尺度模拟不仅推动了BAs冷却衬底在电子器件中的实际应用,还为设计先进热管理系统提供了新方法。

英文摘要:

High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap semiconductor of gallium nitride (GaN) devices has been realized. However, the lack of systematic analysis on the heat transfer across the BAs-GaN interface hampers the practical applications. In this study, by constructing the accurate and high-efficient machine learning interatomic potentials, we performed multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial thermal conductance (ITC) of 265 MW m-2K-1 is achieved, which lies in the well-matched lattice vibrations of BAs and GaN. Moreover, the competition between grain size and boundary resistance was revealed with size increasing from 1 nm to 100 μm. Such deep-potential equipped multiscale simulations not only promote the practical applications of BAs cooling substrates in electronics, but also offer new approach for designing advanced thermal management systems.

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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