Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis

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

doi:10.1021/acs.nanolett.4c05921

Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis

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

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

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 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). We validated the approach using simulated and experimental reflectivity data for WSe₂ growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T′-MoTe₂. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

Original language	English
Pages (from-to)	2444-2451
Number of pages	8
Journal	Nano Letters
Volume	25
Issue number	6
DOIs	https://doi.org/10.1021/acs.nanolett.4c05921
State	Published - Feb 12 2025

Funding

The algorithm development and machine learning were supported by the Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy, Office of Science User Facility, at Oak Ridge National Laboratory, and materials synthesis and modeling was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. F. Bao would like to acknowledge the support from U.S. National Science Foundation through project DMS-2142672 and the support from the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program, under Grant DE-SC0025412. This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05- 00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). Acknowledgments

Keywords

autonomous synthesis
in situ diagnostics
pulsed laser deposition
real-time control
state estimation
thin film synthesis

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10.1021/acs.nanolett.4c05921

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title = "Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis",

abstract = "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 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). We validated the approach using simulated and experimental reflectivity data for WSe2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T′-MoTe2. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15\% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.",

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AU - Harris, Sumner B.

AU - López Fajardo, Ruth Y.

AU - Puretzky, Alexander A.

AU - Xiao, Kai

AU - Bao, Feng

AU - Vasudevan, Rama K.

PY - 2025/2/12

Y1 - 2025/2/12

N2 - 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 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). We validated the approach using simulated and experimental reflectivity data for WSe2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T′-MoTe2. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

AB - 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 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). We validated the approach using simulated and experimental reflectivity data for WSe2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T′-MoTe2. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

KW - autonomous synthesis

KW - in situ diagnostics

KW - pulsed laser deposition

KW - real-time control

KW - state estimation

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Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis

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Keywords

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