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Atomic-Scale Mechanisms of SiO Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential
Jaehoon Kim, Minseok Moon, Hyunsung Cho +5
Plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide (SiO) is widely used for low-temperature fabrication of dielectric thin films, yet its atomic-scale growth…
Atomistic Insights into Cu/amorphous-TaN Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction
Jeong Min Choi, Jaehoon Kim, Ji-Hwan Lee +2
Accurate understanding and control of interfacial adhesion between Cu and TaN diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu inter…
Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
Hyunsung Cho, Minseok Moon, Jaehoon Kim +6
Understanding hydrogen diffusion is critical for improving the reliability and performance of oxide thin-film transistors (TFTs), where hydrogen plays a key role in carrier modulat…
Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
Jaesun Kim, Jisu Kim, Jaehoon Kim +4
Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with reduced…