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.

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.

Acoustic Performance of Stress Gradient-Induced Deflection of Triangular Unimorphic/Bimorphic Cantilevers for MEMS Applications.

This paper reports two piezoelectric materials of lead zirconium titanate (PZT) and aluminum nitride (AlN) used to simulate microelectromechanical system (MEMS) speakers, which inevitably suffered deflections as induced via the stress gradient during the fabrication processes. The main issue is the vibrated deflection from the diaphragm that influences the sound pressure level (SPL) of MEMS speakers. To comprehend the correlation between the geometry of the diaphragm and vibration deflection in cantilevers with the same condition of activated voltage and frequency, we compared four types of geometries of cantilevers including square, hexagon, octagon, and decagon in triangular membranes with unimorphic and bimorphic composition by utilizing finite element method (FEM) for physical and structural analyses. The size of different geometric speakers did not exceed 10.39 mm2; the simulation results reveal that under the same condition of activated voltage, the associated acoustic performance, such as SPL for AlN, is in good comparison with the simulation results of the published literature. These FEM simulation results of different types of cantilever geometries provide a methodology design toward practical applications of piezoelectric MEMS speakers in the acoustic performance of stress gradient-induced deflection in triangular bimorphic membranes.

A Comparison and Analysis of Three Methods of Aluminum Crown Forgings in Processing Optimization.

In this study, three parameter optimization methods and two designs of experiments (DOE) were used for the optimization of three major design parameters ((bill diameter (D), billet length (L), and barrier wall design (BWD)) in crown forging to improve the formability of aluminum workpiece for shock absorbers. The first optimization method is the response surface method (RSM) combined with Box-Behnken's experimental design to establish fifteen (15) sets of parameter combinations for research. The second one is the main effects plot method (MEP). The third one is the multiobjective optimization method combined with Taguchi's experimental design method, which designed nine (9) parameter combinations and conducted research and analysis through grey relational analysis (GRA). Initially, a new type of forging die and billet in the controlled deformation zone (CDZ) was established by CAD (computer-aided design) modeling and the finite element method (FEM) for model simulation. Then, this investigation showed that the optimal parameter conditions obtained by these three optimization approaches (RSM, MEP, and multiobjective optimization) are consistent, with the same results. The best optimization parameters are the dimension of the billet ((D: 40 mm, the length of the billet (L): 205 mm, and the design of the barrier wall (BWD): 22 mm)). The results indicate that the optimization methods used in this research all have a high degree of accuracy. According to the research results of grey relational analysis (GRA), the size of the barrier wall design (BWD) in the controllable deformation zone (CDZ) has the greatest influence on the improvement of the preforming die, indicating that it is an important factor to increase the filling rate of aluminum crown forgings. At the end, the optimized parameters are verified by FEM simulation analysis and actual production validation as well as grain streamline distribution, processing map, and microstructure analysis on crown forgings. The novelty of this work is that it provides a novel preforming die through the mutual verification of different optimization methods to solve a typical problem such as material underfill.

Machine Learning Assisted Classification of Aluminum Nitride Thin Film Stress via In-Situ Optical Emission Spectroscopy Data.

In this study, we submit a complex set of in-situ data collected by optical emission spectroscopy (OES) during the process of aluminum nitride (AlN) thin film. Changing the sputtering power and nitrogen(N2) flow rate, AlN film was deposited on Si substrate using a superior sputtering with a pulsed direct current (DC) method. The correlation between OES data and deposited film residual stress (tensile vs. compressive) associated with crystalline status by X-ray diffraction spectroscopy (XRD), scanning electron microscope (SEM), and transmission electron microscope (TEM) measurements were investigated and established throughout the machine learning exercise. An important answer to know is whether the stress of the processing film is compressive or tensile. To answer this question, we can access as many optical spectra data as we need, record the data to generate a library, and exploit principal component analysis (PCA) to reduce complexity from complex data. After preprocessing through PCA, we demonstrated that we could apply standard artificial neural networks (ANNs), and we could obtain a machine learning classification method to distinguish the stress types of the AlN thin films obtained by analyzing XRD results and correlating with TEM microstructures. Combining PCA with ANNs, an accurate method for in-situ stress prediction and classification was created to solve the semiconductor process problems related to film property on deposited films more efficiently. Therefore, methods for machine learning-assisted classification can be further extended and applied to other semiconductors or related research of interest in the future.