Machine Learning Assisted In-Situ Sensing and Detection on System of PECVD Depositing Hydrogenated Silicon Films

Abstract

Plasma enhanced chemical vapor deposition (PECVD) is commonly known to be used in the field of silicon thin-film solar systems for the application of nanocrystalline silicon (nc-Si:H) film. The chemical deposition is a rather lengthy process, and it is difficult to determine the crystallization and crystalline phase of the thin film prior to X-ray diffraction (XRD) measurements. In this study, we are trying to analyze the spectral data collected by optical emission spectroscopy (OES) to find out there is any correlation between OES data and crystalline status. We used machine learning onto an in-situ detection tool to forecast this correlation. The collected large-scale OES spectral data obtained via principal component analysis (PCA) was used for the prediction of the crystalline phase in films without necessary experiments performed afterwards. Therefore, this method can be applicable to the field of thin film deposition for the detection of properties on thin films.