What we do
From quantum mechanics to reactor scale, VDLab bridges atomistic understanding and process engineering — resolving plasma–material interactions and turning them into predictive, AI-driven virtual process platforms for semiconductor manufacturing.
Physical AI & Digital Twin for Manufacturing
Physics-informed virtual process platforms that combine simulation data with experimental databases. Bridging the virtual and physical worlds, our Physical AI and digital twins target real-time process evaluation, predictive control, and interpretable decision-making, with ultra-precision cryogenic etching as the primary proving ground.
More detail →Multiscale / Multiphysics Simulation
Data-driven computational frameworks bridging atomistic to continuum scales. We resolve plasma-surface interactions at the atomic level, combining machine-learning force fields with molecular dynamics to explore the theoretical limits of challenging processes.
More detail →Computational & Data-Driven Materials & Process Modeling
First-principles and continuum simulations of semiconductor processes (ALD and CVD) and functional materials, integrating deep learning to accelerate process and materials design for next-generation semiconductor materials and selective thin-film processes.
More detail →Active projects
AI-Based Integrated Virtual–Physical Platform for Ultra-Precision Cryogenic Etching
Couples virtual process simulation with physical measurement to model and predict ultra-precision cryogenic etching.
Integrated Virtual–Physical AI for Ultra-Precision Manufacturing of Advanced Semiconductor Devices
A virtual–physical AI digital twin for the fabrication processes of advanced semiconductor devices.
반도체 웨이퍼 나노 구조체 증착 공정의 시뮬레이션 기반 해석 및 예측 플랫폼 구축
Industry-commissioned platform for simulation-based analysis and prediction of nanostructure deposition processes.
MLFF 기반 Autonomous Reaction Map과 CFD 연계를 통한 저온 MoS₂ CVD 기상 반응 해석
MLFF-driven autonomous reaction mapping coupled with CFD for low-temperature MoS₂ CVD gas-phase chemistry.
Advancing Machine Learning Force Fields for Semiconductor Processing with NVIDIA Technologies
GPU-accelerated machine-learning force fields for semiconductor process modeling.
Area Selective Deposition of Novel Metals with 100% Selectivity for Interconnect Technology of Si Devices
Area-selective deposition chemistry and processes for interconnect and electrode technology.
Development of Oxide Semiconductor Materials and Process Technologies for Next-Generation DRAM Applications
Oxide-semiconductor materials and processes for next-generation DRAM devices.
Low-Temperature Chalcogenide Semiconductor Materials with High Mobility for Large-Area 3D Integration
Low-temperature chalcogenide semiconductors with high mobility for large-area 3D integration.
차세대 모빌리티향 센서-뉴로모픽 반도체 지능형 통합시스템 개발
Intelligent integrated sensor–neuromorphic semiconductor systems for next-generation mobility.
반도체특성화대학지원사업
Semiconductor-specialized undergraduate education program at UNIST.
반도체특성화대학원지원사업
Semiconductor-specialized graduate school program (GS-SMDE) at UNIST.
KISTI Innovation Support Program (Supercomputing)
National supercomputing allocation supporting large-scale simulations.
Completed projects
Development of Novel Materials for Cryogenic Etching Process
KISTI Innovation Support Program (Supercomputing)