Bayesian state estimation unlocks real-time control in thin film synthesis

Abstract: The rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable the real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the Direct Filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD) of transition metal dichalcogenides. We validated the approach on simulated and previously acquired reflectivity data for WSe$_2$ growth and ultimately deployed the algorithm on an autonomous PLD system during growth of 1T$^\prime$-MoTe$_2$ under various synthesis conditions. We found that the DF robustly estimates growth parameters in real-time at early stages of growth, down to 15% percent monolayer area coverage. This approach opens new opportunities for adaptive film growth control based on a fusion of in situ diagnostics, modern data assimilation methods, and physical models which promises to enable control of synthesis trajectories towards desired material states.