Machine learning-based modeling and operation for ALD of SiO2 thin-films using data from a multiscale CFD simulation - Ding, Yangyao; Zhang, Yichi; Ren, Yi Ming; Orkoulas, Gerassimos; Christofides, Panagiotis D. (Chemical engineering research & design, 2019)
[Display omitted] •Multiscale computational fluid dynamics (CFD) modeling of ALD reactor.•Machine-learning modeling using multiscale CFD model data.•Use of machine learning model to optimize ALD cycle time.•Significant reduction of ALD cycle time versus fixed-time deposition. Atomic layer deposition (ALD) is a widely utilized deposition technology in the semiconductor industry due to its superior ability to generate highly conformal films and to deposit materials into high aspect-ratio geometric structures. However, ALD experiments remain expensive and time-consuming, and the existing first-principles based models have not yet been able to provide solutions to key process outputs that are computationally efficient, which is necessary for on-line optimization and real-time control. In this work, a multiscale data-driven model is proposed and developed to capture the macroscopic process domain dynamics with a linear parameter varying model, and to characterize the microscopic domain film growth dynamics with a feed-forward artificial neural network (ANN) model. The multiscale data-driven model predicts the transient deposition rate from the following four key process operating parameters that can be manipulated, measured or estimated by process engineers: precursor feed flow rate, operating pressure, surface heating, and transient film coverage. Our results demonstrate that the multiscale data-driven model can efficiently characterize the transient input-output relationship for the SiO2 thermal ALD process using bis(tertiary-butylamino)silane (BTBAS) as the Si precursor. The multiscale data-driven model successfully reduces the computational time from 0.6 to 1.2h for each time step, which is required for the first-principles based multiscale computational fluid dynamics (CFD) model, to less than 0.1s, making its real-time usage feasible. The developed data-driven modeling methodology can be further generalized and used for other thermal ALD or similar deposition systems, which will greatly enhance the feasibility of industrial manufacturing processes.