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arXiv · 2410.23895

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

Sumner B. Harris·Ruth Fajardo·Alexander A. Puretzky·Kai Xiao·Feng Bao·Rama K. Vasudevan
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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.

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Sumner B. Harris, Ruth Fajardo, Alexander A. Puretzky, Kai Xiao, Feng Bao, Rama K. Vasudevan. 2024-10-31. Bayesian state estimation unlocks real-time control in thin film synthesis. https://doi.org/10.1021/acs.nanolett.4c05921.

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