Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning. (January 2021)

• Record Type: Journal Article
• Authors: Ohkubo, I.; Hou, Z.; Lee, J.N.; Aizawa, T.; Lippmaa, M.; Chikyow, T.; Tsuda, K.; Mori, T.
• Is Part Of: Materials today physics. Volume 16(2020)
• Publication Date: 2021-01
• Subjects: Molecular beam epitaxy -- Machine learning -- Superconductors -- Transition metal nitrides; Materials science -- Periodicals; Physics -- Periodicals; Electronic journals; 530.41
• DOI: 10.1016/j.mtphys.2020.100296
• Languages: English
• ISSNs: 2542-5293

Abstract

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. Graphical abstract: Image 1 Highlights: Closed-loop optimization of epitaxial TiN thin-film growth was demonstrated using a Bayesian machine-learning technique. The suitable growth conditions were obtained after eleven thin-film growth experiments. Superconducting transitions appear above 5 K. High-quality epitaxial transition metal nitrides can be grown via MO-MBE.