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Related Experiment Video

Updated: Jun 13, 2025

Plasma-Assisted Molecular Beam Epitaxy Growth of Mg3N2 and Zn3N2 Thin Films

Plasma-Assisted Molecular Beam Epitaxy Growth of Mg3N2 and Zn3N2 Thin Films

Published on: May 11, 2019

Multimodal Machine Learning Analysis of GaSe Molecular Beam Epitaxy Growth Conditions.

Mingyu Yu¹, Isaiah A Moses², Wesley F Reinhart^3,4

¹Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States.

ACS Applied Materials & Interfaces

|May 28, 2025

Summary

Machine learning enhances molecular beam epitaxy (MBE) for gallium selenide (GaSe) thin-film growth. Integrating reflection high-energy electron diffraction (RHEED) with AI optimizes film quality and accelerates synthesis.

Keywords:

autonomous synthesis platform in situ diagnostics machine learning molecular beam epitaxy van der Waals chalcogenides

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Last Updated: Jun 13, 2025

Plasma-Assisted Molecular Beam Epitaxy Growth of Mg3N2 and Zn3N2 Thin Films

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Published on: May 11, 2019

Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization

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Published on: July 17, 2015

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Published on: November 24, 2016

Area of Science:

Materials Science
Chemical Engineering
Artificial Intelligence

Background:

Autonomous synthesis platforms offer real-time optimization for thin-film growth.
Molecular beam epitaxy (MBE) applications for autonomous synthesis are underdeveloped.
Machine learning (ML) and in situ diagnostics can revolutionize thin-film fabrication.

Purpose of the Study:

Develop an ML-guided framework for MBE growth of gallium selenide (GaSe) films.
Utilize reflection high-energy electron diffraction (RHEED) as an in situ diagnostic.
Correlate RHEED patterns with film quality metrics.

Main Methods:

Applied unsupervised learning to RHEED patterns for quality classification.
Performed mutual information analysis to link RHEED features with film properties (fwhm, RMS roughness).
Developed supervised ML models to predict film quality and used anomaly detection.

Main Results:

Unsupervised learning on RHEED identified distinct high- and low-quality sample boundaries.
RHEED embeddings strongly correlated with rocking curve full-width at half-maximum (fwhm).
ML models incorporating RHEED embeddings significantly improved prediction accuracy for fwhm and RMS roughness.

Conclusions:

Established a data-driven framework for ML-assisted MBE.
Demonstrated the potential of RHEED-based ML for real-time process control.
Paved the way for accelerated optimization in thin-film synthesis.