Liu Xiangjun, Wang Di, Wang Baolong, Wang Quanjie, Sun Jisheng, Xiong Yucheng
Institute of Micro/Nano Electromechanical System, Donghua University, Donghua University, Shanghai, 201620, CHINA.
Donghua University, Donghua University, Shanghai, 201620, CHINA.
J Phys Condens Matter. 2024 Sep 25. doi: 10.1088/1361-648X/ad7fb0.
Efficient heat dissipation is crucial for the performance and lifetime of high electron mobility transistors (HEMTs). The thermal conductivity of materials and interfacial thermal conductance (ITC) play significant roles in their heat dissipation. To predict the thermal properties of AlxGa1-xN and the ITC of GaN/AlxGa1-xN in HEMTs, a dataset with first-principles accuracy was constructed using concurrent learning method and trained to obtain an interatomic potential employing deep neural networks (DNN) method. Using obtained DNN interatomic potential, equilibrium molecular dynamics simulations were employed to calculate the thermal conductivity of AlxGa1-xN, which showed excellent consistent with experimental results. Additionally, the phonon density of states of AlxGa1-xN and the ITC of GaN/AlxGa1-xN were calculated. Our study revealed a decrease in the ITC of GaN/AlxGa1-xN with increasing x, and the insertion of 1nm-thick AlN at the interface significantly reduced the ITC. This work provided a high-fidelity DNN potential for molecular dynamics simulations of AlxGa1-xN, offering valuable guidance for exploring the thermal transport of complex alloy and heterostructure.
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高效散热对于高电子迁移率晶体管（HEMT）的性能和寿命至关重要。材料的热导率和界面热导（ITC）在其散热过程中起着重要作用。为了预测HEMT中AlxGa1-xN的热性能以及GaN/AlxGa1-xN的ITC，使用并发学习方法构建了具有第一性原理精度的数据集，并采用深度神经网络（DNN）方法进行训练以获得原子间势。利用获得的DNN原子间势，采用平衡分子动力学模拟计算AlxGa1-xN的热导率，结果与实验结果显示出极好的一致性。此外，还计算了AlxGa1-xN的声子态密度以及GaN/AlxGa1-xN的ITC。我们的研究表明，随着x的增加，GaN/AlxGa1-xN的ITC降低，并且在界面处插入1nm厚的AlN会显著降低ITC。这项工作为AlxGa1-xN的分子动力学模拟提供了一个高保真的DNN势，为探索复杂合金和异质结构的热输运提供了有价值的指导。
