Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning

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 esuperconductor 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. (C) 2020 The Authors. Published by Elsevier Ltd.

Automated summary

The closed-loop optimization of epitaxial titanium nitride thin-film growth was achieved using metal-organic molecular beam epitaxy (MO-MBE) technique combined with Bayesian machine learning, reducing the number of growth experiments. Epitaxial TiN thin films grown under the optimized conditions exhibited abrupt superconductor transitions above 5 K, showing a new efficient approach for developing less-studied materials. The combination of thin-film growth technique and Bayesian approach is expected to accelerate the development of automated operation of thin-film growth apparatuses.