Researchers at University of Tsukuba have developed a technology for real-time estimation of the valence state and growth rate of iron oxide thin films during their formation. This novel technology was realized by analyzing the full-wavelength data of plasma emission spectra generated during reactive sputtering using machine learning. It is expected to enable high-precision control of the film deposition process.

Metal oxide and nitride thin films are commonly used in electronic devices and energy materials. Reactive sputtering is a versatile technique for depositing thin films by reacting a target metal with gases such as oxygen or nitrogen. A challenge with this process is the transitioning of the target surface between metallic and compound states, causing large fluctuations in film growth rate and composition. At present, there are limited effective methods for real-time monitoring of a material’s chemical state and deposition rate during film formation.

A machine learning technique based on principal component analysis was employed to examine massive emission spectra generated within a reactive sputter plasma. This analysis focused on assessing the state of thin film formation. The results, published in Science and Technology of Advanced Materials: Methods, indicated that the valence state of iron oxide thin films was accurately identified using only the first and second principal components of the spectra. In addition, the film growth rate was predicted with high precision.