[8a-N403-1~10] 13.1 Fundamental properties, surface and interface, and simulations of Si related materials

10:45 AM - 11:00 AM JST(1:45 AM - 2:00 AM UTC)

[8a-N403-7] Molecular dynamics study of a-(SiO₂)_x(Al₂O₃)_1-x /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

Forming a high-quality gate insulator is essential for the practical implementation of GaN-based devices, yet oxidations and defects induced between the insulator and GaN substrate often lead to degradation of the device performance. In this study, we assessed the structure and defects of the a-AlSiO/GaOx/GaN interface through MD simulations employing a machine-learning interatomic potential and subsequent DFT analysis. Pure a-Al₂O₃ resulted in significant increase of the defect density due to recrystallization during annealing, whereas SiO₂ incorporation promoted Ga–O passivation and effectively suppressed in-gap defect states. Furthermore, inserting and precisely controlling the GaOx layer was shown to effectively improve the Ga–O passivation.