가상재료공정설계 연구실

VDLab investigates phenomena that span scales, turns that understanding into virtual models for materials, processes, and equipment, and connects those models to experiments and physical manufacturing through AI. Semiconductor manufacturing is our primary proving ground, while functional materials broaden the design space. Many engineering systems are governed by phenomena that cross length, time, and physical domains. We develop first-principles, atomistic, mesoscale, feature-scale, and continuum models, then connect them with physics-based and data-driven scale bridging. The result is not a collection of isolated simulations but a predictive description that carries mechanisms and uncertainty across scales. We turn computational understanding into design variables and decisions. Our work spans semiconductor surfaces and interfaces, etching and deposition, film growth, reactor transport, and equipment conditions, with AI and optimization used to explore spaces that are too large for trial and error. The same methodology extends to broader functional materials. Advanced Manufacturing AI begins when virtual models and physical systems share information. We combine simulation, experimental databases, in-situ sensing, and reduced-order models in digital twins that can estimate hidden process states, explain predictions, and support optimization and control. Physical AI and agentic systems are the path toward closing this loop from observation to action. Couples virtual process simulation with physical measurement to model and predict ultra-precision cryogenic etching. A virtual–physical AI digital twin for the fabrication processes of advanced semiconductor devices. Industry-commissioned platform for simulation-based analysis and prediction of nanostructure deposition proc