Disruptive 1st-Principles TCAD
Based on MS-DFT and related computational methods, we are actively developing science and technology that will enable hitherto unachievable technology computer-aided design (TCAD) of next-generation semiconductor devices. We recently demonstrated for the first time ab initio device simulations of graphene-based van der Waals 2D heterojunction transistors. A key focus of our present effort is extending the length and time scales of first-principles electronic structure and TCAD calculations by incorporating artificial intelligence (AI) and multi-scale computing techniques. A notable recent development is the DeepSCF model, which provides a foundational principle for AI-based acceleration of materials simulations across scales.
Selected Publications
● "Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints"
Ryong-Gyu Lee and Yong-Hoon Kim
npj Computational Materials, Vol. 10, Art. 248 (2024)
Media Coverage: KAIST News (in Korean), KAIST News, and International Media
● "Gate-versus defect-induced voltage drop and negative differential resistance in vertical graphene heterostructures"
Tae Hyung Kim, Juho Lee, Ryong-Gyu Lee, and Yong-Hoon Kim
npj Computational Materials, Vol. 8, Art. 50 (2022)
Media Coverage: KAIST Breakthroughs (Spring 2023), KAIST News (in Korean), and KAIST Times (in Korean)