講演情報
[11p-E206-12]A Unified High-κ Dielectric Screening Pipeline Based on a Machine Learning Hamiltonian
〇(B)Doyun Kim1,2, Dongik Park2, Juhan Hong1,3, Jaewon Bae2, Chanyoung Park2,3 (1.Seoul Natl. Univ., 2.NanoForge AI, 3.KAIST)
キーワード：
High-k dielectrics、machine learning Hamiltonian、materials screening
High-κ gate dielectrics are essential for semiconductor device scaling, but candidate oxides must combine a high static dielectric constant with a wide band gap. We present a unified high-κ dielectric screening pipeline based on a machine learning Hamiltonian (MLH). The predicted Kohn-Sham Hamiltonian is used to obtain the band gap, electronic dielectric contribution, and ionic dielectric contribution in a unified manner. This enables internally consistent screening of oxide materials for high-κ applications. We validate the pipeline on dynamically stable oxides held out from training and then apply it to a broader oxide screening study.