[15a-K509-1~6] 13.1 Fundamental properties, surface and interface, and simulations of Si related materials

11:00 AM - 11:15 AM JST(2:00 AM - 2:15 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[15a-K509-5] Molecular dynamics study of a-Al₂O₃/GaN interface using machine learning force field

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

Keywords:

machine learning,gallium nitride,first-principles calculation

To address challenges in GaN-based semiconductor devices, the a-Al2O3/GaOx/GaN interface was analyzed using molecular dynamics simulations. Large-scale simulations with machine learning potentials demonstrated that oxygen passivation effectively reduces defect states, and the insertion of a GaOx layer significantly decreases defect density. This study suggests the potential for improving device performance through optimization of oxygen partial pressure and growth/annealing conditions of the insulating film.