Impact of Pulse Parameters of a DC Power Generator on the Microstructural and Mechanical Properties of Sputtered AlN Film with In-Situ OES Data Analysis

• Published in: Materials Vol. 16; no. 8; p. 3015
• Main Authors: Zhou, Wei-Yu, Chen, Hsuan-Fan, Tseng, Xue-Li, Lo, Hsiao-Han, Wang, Peter J, Jiang, Ming-Yu, Fuh, Yiin-Kuen, Li, Tomi T
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11-04-2023; MDPI
• Subjects: Accuracy; AlN; Aluminum compounds; Aluminum nitride; Analysis; Artificial intelligence; Atomic force microscopy; BoxBehnken method; Data analysis; Deep learning; Diffraction; Electric potential; Energy consumption; Field emission microscopy; Grain size; In-situ OES; Independent variables; Machine learning; Mathematical models; Mechanical properties; Microstructure; Optical emission spectroscopy; Parameter identification; PCA; Plasma etching; Power supply; Principal components analysis; Process controls; Pulse parameter; Reactive pulsed DC magnetron sputtering; Residual stress; Response surface methodology; Scanning electron microscopy; Surface roughness; Thickness; Thin films; Voltage; X-ray diffraction; X-rays; Taiwan; AlN; CatBoost modelling; BoxBehnken method; In-situ OES; Reactive pulsed DC magnetron sputtering; Pulse parameter; RSM; PCA
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma16083015

In the present study, the sputtered aluminum nitride (AlN) films were processed in a reactive pulsed DC magnetron system. We applied a total of 15 different design of experiments (DOEs) on DC pulsed parameters (reverse voltage, pulse frequency, and duty cycle) with Box-Behnken experimental method and response surface method (RSM) to establish a mathematical model by experimental data for interpreting the relationship between independent and response variables. For the characterization of AlN films on the crystal quality, microstructure, thickness, and surface roughness, X-ray diffraction (XRD), atomic force microscopy (AFM), and field emission-scanning electron microscopy (FE-SEM) were utilized. AlN films have different microstructures and surface roughness under different pulse parameters. In addition, in-situ optical emission spectroscopy (OES) was employed to monitor the plasma in real-time, and its data were analyzed by principal component analysis (PCA) for dimensionality reduction and data preprocessing. Through the CatBoost modeling and analysis, we predicted results from XRD in full width at half maximum (FWHM) and SEM in grain size. This investigation identified the optimal pulse parameters for producing high-quality AlN films as a reverse voltage of 50 V, a pulse frequency of 250 kHz, and a duty cycle of 80.6061%. Additionally, a predictive CatBoost model for obtaining film FWHM and grain size was successfully trained.