Machine Learning Modeling and Run-to-Run Control of an Area-Selective Atomic Layer Deposition Spatial Reactor

Abstract

Semiconducting materials require stringent design specifications that make their fabrication more difficult and prone to flaws that are costly and damaging to their computing and electrical properties. Area-selective atomic layer deposition is a process that addresses concerns associated with design imperfections but requires substantial monitoring to ensure that process regulation is maintained. This work proposes a run-to-run controller with an exponentially weighted moving average method for an area-selective atomic layer deposition rotary reactor by adjusting the rotation speed of the substrate to control the growth per cycle of the wafer, which is calculated through a multiscale model with machine learning integration for pressure field generation and kinetic Monte Carlo simulations to increase computational efficiency. Results indicate that the run-to-run controller was able to bring the process to the setpoint when subjected to moderate pressure and kinetic shift disturbances.