论文标题
硼芳烃冷却基板上的氮化炮器设备的深度电势仿真模拟
Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates
论文作者
论文摘要
高效散热对于高功率密度电子产品起关键作用。超高热电导率硼（BAS，1300 W M-1K-1）冷却底物的实验合成已经实现了宽带氮化凝剂（GAN）设备的宽带袋中半导体。但是，缺乏对跨杆界面的热传递的系统分析会阻碍实际应用。在这项研究中，通过构建准确，高效的机器学习间原子潜能，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的超高界面热电导（ITC），这是BAS和GAN匹配的晶格振动。此外，晶粒尺寸和边界电阻之间的竞争被揭示，尺寸从1 nm增加到100μm。这样的具有深度能力的多尺度模拟不仅促进了电子中的BAS冷却基板的实际应用，而且还为设计高级热管理系统提供了新的方法。
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.
