AI-powered precursor quantification in atmospheric pressure plasma jet thin film deposition via optical emission spectroscopy

Abstract This study explores the feasibility of using Optical Emission Spectroscopy (OES) for in situ monitoring of Atmospheric Pressure Plasma Jet systems in the deposition of thin films. We identify process parameters to control film properties by machine learning for data analysis. In experiments, the depth of the carrier gas inlet pipe (pipe depth) is a crucial controllable variable that directly affects the amount of precursor, influencing the film’s thickness, sheet resistance, and resistivity. We collected 96 000 spectra while preparing 12 film samples, subsequently measured the properties of the samples, and analyzed the spectral data using Principal Component Analysis (PCA) and seven supervised machine learning models. A high correlation was found between spectral features and film thickness. We divided the spectral data in a single process based on processing time into the first third (F-third) and the last third (L-third). Using the F-third data, the PCA plot clearly indicated a significant difference between the two pipe depths, achieving a mean recognition accuracy of 95.1% with machine learning models. In contrast, using the L-third data, the PCA plot showed a high degree of overlap between the two pipe depths, resulting in a considerable decline in recognition performance. Overall, it is challenging to distinguish the spectra visually due to variations in precursor amounts and dynamic fluctuations in the OES signals, even after averaging. Nonetheless, through the successful application of machine learning, we demonstrated an effective spectral recognition system for monitoring pipe depth, which aids in the timely control of film properties.