With first-principles and continuum simulations, we model semiconductor processes, including atomic layer deposition (ALD) and chemical vapor deposition (CVD), and functional materials, integrating deep learning to accelerate process and materials design. The work feeds directly into conductive metal films (Ru, WC), area-selective deposition, and oxide semiconductors for next-generation memory.
We model ALD and plasma-enhanced ALD, area-selective ALD, and CVD processes from first principles. Currently we use DFT and MD to screen substrate-driven selectivity and inhibitor chemistries for area-selective ALD of ZnO and Ru. The target is reliable bottom-up patterning.
We design selective metal thin-film processes, including Ru and Mo with inhibitor-driven selectivity. Recent work covers first-principles-guided PE-ALD of highly conductive tungsten carbide and atomic-hydrogen-driven area-selective ruthenium deposition. The aim is low-resistance films that pattern themselves.
We model oxide semiconductor materials for next-generation DRAM and compute-in-memory. Currently this means indium-tin-oxide vertical-channel transistors for monolithic 3D compute-in-memory and 2T0C gain-cell memory. The modeling feeds directly into device-level design.
We study low-temperature, chalcogenide-based high-mobility semiconductor materials. Currently we model MOCVD growth mechanisms and strain engineering of transition-metal dichalcogenides (MoS₂) for low-power electronics. The focus is getting high mobility at process-compatible temperatures.
Across all of the above we layer deep learning and Bayesian optimization to accelerate materials and process design. Instead of exhaustive search, we let data-driven models propose the next experiment. This shortens the loop from candidate to validated process.
ALD / PE-ALD & area-selective ALD
CVD / MOCVD growth
kMC deposition
DFT (VASP, Quantemol)
Adsorption / reaction energetics
Bayesian optimization
Deep-learning property models
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
Oxygen-Tunnel Indium Tin Oxide Vertical Channel Transistors with Enhanced Current Density and Reliability for Monolithic 3D Compute-In-Memory Systems
Indium tin oxide vertical-channel transistors for scaled 4F² 2T0C gain cell memory with etched sidewall cleaning
Polyaniline as an integrated amine-redox platform for reversible electrosorption and energy-efficient PFAS remediation: from molecular mechanisms to system integration