Density Functional Theory (DFT)-enhanced computational fluid dynamics modeling of substrate movement and chemical deposition process in spatial atomic layer deposition

Abstract Spatial atomic layer deposition (ALD) has been widely accepted as an effective strategy to improve the throughput in traditional ALD. This paper presents a Density Functional Theory (DFT)-enhanced Computational Fluid Dynamics (CFD) model, which includes substrate movement and a mechanistic chemical kinetics model derived from DFT in an inline spatial ALD. Substrate movement alters the steady flow field, but the disturbance does not damage the effective gas barrier built by N2 flow. Evaluation of deposition rate shows the growth per cycle (GPC) decreases as the substrate velocity increases. However, the growth per second (GPS) increases because of the considerable amount of time saved in faster substrate movements. Bottom and top purging designs are shown comparable in deposition rate. The DFT-enhanced CFD model provides a new route for accurate ALD modeling and simulation, and offers insightful information on reactor design and process optimization in spatial ALD.