Multiscale computational fluid dynamics modeling of an area-selective atomic layer deposition process using a discrete feed method

Abstract

Area-selective atomic layer deposition (AS-ALD) is a beneficial procedure that facilitates self-alignment for transistor stacking by concentrating oxide growth on targeted areas of a substrate. However, AS-ALD is difficult to incorporate into semiconductor manufacturing industries due to difficulties such as minimal process data and a lack of insight into reactor design. To enable the industrial scale-up of AS-ALD, in silico modeling is necessary to characterize the process. Thus, this work proposes a multiscale computational fluid dynamics modeling framework that simultaneously describes the surface chemistry and ambient fluid behavior for an Al₂O₃/SiO₂ substrate. The multiscale model first involves ab initio molecular dynamics simulations to optimize molecular structures involved in the AS-ALD reactions. Next, a kinetic Monte Carlo simulation is performed to describe the stochastic surface chemistry behavior to determine the surface coverage, and deposition and byproduct rates. Lastly, computational fluid dynamics is performed to study the spatiotemporal behavior of the flow. The surface and flow field simulations are carried out in an integrated fashion. Various AS-ALD discrete feed reactor configurations with differing injection plate geometries were developed to investigate their impact on the processing time to achieve full surface coverage and film uniformity. Results indicate that the multi-inlet reactor model achieves minimal processing time while producing a high-quality film with the AS-ALD process.