Using data-driven computational frameworks that bridge atomistic to continuum scales, we model complex engineering systems with coupled physical phenomena. We resolve plasma-surface interactions in semiconductor processing at the atomic level, combining machine-learning force fields (MLFF) with molecular dynamics to explore the theoretical limits of challenging processes.
We bridge quantum, atomistic, continuum, and reactor scales in a single data-driven framework. This lets us model complex engineering systems where coupled physical phenomena cross length scales. It is the backbone that ties our atomistic insight to process- and reactor-scale behavior.
We resolve plasma-surface interactions such as etching, plasma annealing, and sputtering at the atomic level. Currently we study the atomistic mechanism of low-temperature NF₃/H₂ etching of SiO₂-SiN-SiO₂ stacks, which underlies cryogenic selectivity, and ion-bombardment-driven crystallization with pressure-dependent radical transport (e.g. plasma-assisted crystallization of Al-doped TiO₂).
Molecular dynamics (including reactive MD), DFT, and first-principles calculations are our core engines. We currently apply reactive MD to plasma-activated SiCN bonding and Cu-Cu diffusion bonding, quantifying bonding energy and interfacial void formation. These feed structural insight back into process design.
Machine-learning force fields and neural-network potentials extend MD toward first-principles accuracy at scale. We train them for etching, deposition, and 2D-material systems where classical potentials fall short. This is what makes large, reactive simulations tractable.
At the largest scale we model feature-scale profile evolution with DSMC transport and level-set methods. This connects atomistic reaction data to the shapes that actually form on the wafer. It closes the multiscale ladder from atoms to features.
LAMMPS
Reactive MD (ReaxFF)
OVITO analysis
DFT (VASP)
Ab-initio MD
MLFF / neural-network potentials (SevenNet)
Plasma / DSMC transport
Level-set profile evolution
Structural characterization and bonding energy analysis for plasma-activated bonding of SiCN films: A reactive molecular dynamics study
Molecular dynamics study of grain size effects on interfacial voids in Cu–Cu diffusion bonding
In-Situ Post-Doping Plasma Process during ALD of Al-Doped TiO₂ for Sub-Nanometer Lattice Ordering and Defect Annihilation
Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation
Combined molecular dynamics simulations and reaction kinetics study on wettability of Trimethylsilyl functionalized silicon surfaces