講演情報
[7p-N105-6]Revisiting van der Waals interactions in AlN-Copper via Machine Learning Potential
〇(D)Wang Weitao1, Yunhui Wu1, Sebastian Volz2, Masahiro Nomura1 (1.The Univ. of Tokyo, 2.LIMMS)
キーワード：
Machine Learning Potential、Molecular Dynamic Simulation、Thermal Boundary Resistance
The AlN-Copper interface, formed by AlN with high thermal conductivity and electrical insulation and Copper (Cu), is a critical and indispensable structure in fields such as power modules, LED packaging, microwave circuits, and insulated metal substrates. However, the relatively weak van der Waals interactions and potential structural mismatches at the interface may lead to increased thermal resistance [1]. Therefore, understanding and optimizing interfacial interactions at the atomic scale is of great significance for improving the performance and reliability of next-generation electronic systems. The traditional approach to investigate the thermal transport characteristics at AlN-Copper interface is using molecular dynamic (MD) simulation with empirical potential [2]. However, with the development of machine learning potential, many research have shown that there are errors in the interface behaviors described by empirical potential [3]. To examine the van der Waals interaction between Si and Cu, the machine learning potential (MLP) for AlN-Cu is first established, and the thermal boundary resistance (TBR) are evaluated by both empirical potential and MLP. Further, the Cu stacking morphologies on AlN are constructed with crystal and deposit Cu. From the atomic level, the density of states analysis and phonon transmission coefficient are utilized to explain the impact of various interface configurations on thermal transport. In addition, phonon wave packet simulations further revealed the influence of atomic ordering at the interface on phonon transport. This work provides valuable insights into understanding the interfacial thermal transport between metal and nitride semiconductors and useful guidance for thermal management via interface engineering.