Computational models become most valuable when they change a design decision. We translate multiscale understanding into virtual environments where material composition, surface chemistry, process conditions, geometry, and equipment parameters can be evaluated together. Our primary domain is semiconductor manufacturing, spanning materials and interfaces, etching and deposition, feature evolution, and reactor transport. The same framework supports the design and optimization of broader functional materials.
We study how composition, defects, microstructure, and interfaces govern electronic, mechanical, thermal, and chemical behavior. DFT, molecular dynamics, and machine-learning force fields reveal mechanisms in conductive films, oxide semiconductors, two-dimensional materials, and bonding interfaces. These mechanisms become physically meaningful design variables for materials and downstream processes.
Etching is a coupled materials–process–equipment problem. We connect equipment conditions and plasma states to the species, energies, and angles arriving at the wafer, then resolve the resulting surface reactions, implantation, sputtering, and material removal. Current applications include cryogenic and high-aspect-ratio etching, plasma annealing, and profile evolution.
We model the chemical and physical steps by which thin films form. First-principles and atomistic simulations resolve adsorption, ligand elimination, surface activation, nucleation, and inhibitor selectivity, while kinetic and transport models extend these mechanisms to film growth and feature coverage. Applications include ALD, PEALD, area-selective ALD, CVD, and MOCVD.
Equipment-level conditions determine the environment in which surface reactions occur. CFD, plasma chemistry, and particle-transport models connect power, pressure, flow, temperature, and reactor geometry to the reactive species and energy delivered to the wafer. Coupling these models with surface simulation reveals how machine-level parameters drive material evolution.
The same virtual design methodology extends beyond semiconductor manufacturing. Our work includes functional polymers and nanocomposites, electro-functional materials for environmental applications, energy-absorbing structures, oxide semiconductors, and two-dimensional or chalcogenide materials. Across these systems, the common problem is to connect composition, molecular structure, interfaces, and processing to a target function.
AI and optimization make large design spaces computationally tractable. Surrogate models, Bayesian and multi-objective optimization, active learning, and inverse-design strategies identify promising materials, structures, and process windows without exhaustive trial and error. Physics-based models provide constraints and interpretable descriptors, while data-driven methods determine where to search next.
DFT
MD
MLFF
Electronic and structural descriptors
Reaction pathways
Surface kinetics
kMC
Feature evolution
CFD
Chemkin
DSMC
Reactor and plasma modeling
Surrogate models
Bayesian optimization
Multi-objective optimization
Active learning
Experimental databases
Process measurements
Material and device metrics
Highly Conductive Tungsten Carbide Thin Films with a Suppressed Size Effect Prepared by Plasma-Enhanced Atomic Layer Deposition
Mechanistic Atomic Hydrogen Chemistry for Ruthenium Deposition: From Ligand Elimination to Area-Selective Patterning
In-situ post-doping plasma process during atomic layer deposition of Al-doped TiO₂ for sub-nanometer lattice ordering and defect annihilation
Polyaniline as an Integrated Amine-Redox Platform for Reversible Electrosorption and Energy-Efficient PFAS Remediation: From Molecular Mechanisms to System Integration
Efficient design of Voronoi energy-absorbing foams using Bayesian optimization