Abstract Closed-loop optimization of epitaxial titanium nitride (TiN) thin-film growth was accomplished using metal-organic molecular beam epitaxy (MO-MBE) combined with a Bayesian machine-learning technique and reduced the required number of thin-film growth experiments. Epitaxial TiN thin films grown under the process conditions optimized by the Bayesian approach exhibited abrupt metal–superconductor transitions above 5 K, demonstrating a new approach to the efficient development of less-studied materials, such as transition metal nitrides. The combination of the thin-film growth technique and Bayesian approach is expected to pave the way toward accelerating the development of the automated operation of thin-film growth apparatuses.

中文翻译：

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通过机器学习实现外延氮化钛薄膜生长的闭环优化

摘要 利用金属有机分子束外延 (MO-MBE) 结合贝叶斯机器学习技术完成了外延氮化钛 (TiN) 薄膜生长的闭环优化，并减少了所需的薄膜生长实验次数。在通过贝叶斯方法优化的工艺条件下生长的外延 TiN 薄膜在 5 K 以上表现出突然的金属 - 超导体转变，展示了一种有效开发研究较少的材料（例如过渡金属氮化物）的新方法。薄膜生长技术和贝叶斯方法的结合有望为加速薄膜生长设备自动化操作的发展铺平道路。