[20p-C43-1~11] 13.1 Fundamental properties, surface and interface, and simulations of Si related materials

2:00 PM - 2:15 PM JST(5:00 AM - 5:15 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[20p-C43-5] Defect state analysis of a-Al₂O₃/GaN interface using machine learning potential MD

〇Koki Sato¹, Mutsunori Uenuma², Ryousuke Jinnouchi¹, Ryoji Asahi¹ (1.Nagoya Univ., 2.AIST)

Keywords:

machine learning,gallium nitride,first-principles calculation

In semiconductor devices based on gallium nitride, which is expected to be a next-generation semiconductor, defects at an interface with amorphous alumina affect significantly on device properties. In this study, we investigated an influence of the "atomistic microstructure" of the interface on the "macroscopic properties" (defect levels) using a machine learning potential that is trained with the first-principles calculations. The large-scale molecular dynamics simulations showed that lack of the passivation by oxygen and non-bonding states between Ga and N lead to defect states in the GaN band gap.