Prediction by a hybrid machine learning model for high-mobility amorphous In2O3: Sn films fabricated by RF plasma sputtering deposition using a nitrogen-mediated amorphization method

Abstract

In this study, we developed a hybrid machine learning technique by combining appropriate classification and regression models to address challenges in producing high-mobility amorphous In2O3:Sn (a-ITO) films, which were fabricated by radio-frequency magnetron sputtering with a nitrogen-mediated amorphization method. To overcome this challenge, this hybrid model that was consisted of a support vector machine as a classification model and a gradient boosting regression tree as a regression model predicted the boundary conditions of crystallinity and experimental conditions with high mobility for a-ITO films. Based on this model, we were able to identify the boundary conditions between amorphous and crystalline crystallinity and thin film deposition conditions that resulted in a-ITO films with 27% higher mobility near the boundary than previous research results. Thus, this prediction model identified key parameters and optimal sputtering conditions necessary for producing high-mobility a-ITO films. The identification of such boundary conditions through machine learning is crucial in the exploration of thin film properties and enables the development of high-throughput experimental designs.