Jing Wu1, E Zhou1, An Huang1, Hongbin Zhang2, Ming Hu3, Guangzhao Qin1,*
The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.012552
Abstract High thermal conductivity substrate plays a significant role for efficient heat dissipation of electronic devices, and it is urgent to optimize the interfacial thermal resistance. As a novel material with ultra-high thermal conductivity second only to diamond, boron arsenide (BAs) shows promising applications in electronics cooling [1,2]. By adopting multi-scale simulation method driven by machine learning potential, we systematically study the thermal transport properties of boron arsenide, and further investigate the interfacial thermal transport in the GaN-BAs heterostructures. Ultrahigh interfacial thermal conductance of 260 MW m-2K-1 is achieved, which agrees well with experimental measurements, and the More >
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