scholarly journals Investigation of Biocompatible PEO Coating Growth on cp-Ti with In Situ Spectroscopic Methods

Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 9
Author(s):  
Veta Aubakirova ◽  
Ruzil Farrakhov ◽  
Arseniy Sharipov ◽  
Veronika Polyakova ◽  
Lyudmila Parfenova ◽  
...  
Keyword(s):  
Impedance Spectroscopy ◽  
Spectral Line ◽  
Coating Thickness ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Treatment Time ◽  
Optical Emission ◽  
Functional Materials ◽  
Cp Ti

The problem of the optimization of properties for biocompatible coatings as functional materials requires in-depth understanding of the coating formation processes; this allows for precise manufacturing of new generation implantable devices. Plasma electrolytic oxidation (PEO) opens the possibility for the design of biomimetic surfaces for better biocompatibility of titanium materials. The pulsed bipolar PEO process of cp-Ti under voltage control was investigated using joint analysis of the surface characterization and by in situ methods of impedance spectroscopy and optical emission spectroscopy. Scanning electron microscopy, X-ray diffractometry, coating thickness, and roughness measurements were used to characterize the surface morphology evolution during the treatment for 5 min. In situ impedance spectroscopy facilitated the evaluation of the PEO process frequency response and proposed the underlying equivalent circuit where parameters were correlated with the coating layer properties. In situ optical emission spectroscopy helped to analyze the spectral line evolutions for the substrate material and electrolyte species and to justify a method to estimate the coating thickness via the relation of the spectral line intensities. As a result, the optimal treatment time was established as 2 min; this provides a 9–11 µm thick PEO coating with Ra 1 µm, 3–5% porosity, and containing 75% of anatase. The methods for in-situ spectral diagnostics of the coating thickness and roughness were justified so that the treatment time can be corrected online when the coating achieves the required properties.

scholarly journals Machine Learning Prediction of Electron Density and Temperature from Optical Emission Spectroscopy in Nitrogen Plasma

Coatings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1221
Author(s):  
Jun-Hyoung Park ◽  
Ji-Ho Cho ◽  
Jung-Sik Yoon ◽  
Jung-Ho Song
Keyword(s):  
Machine Learning ◽  
Electron Temperature ◽  
Real Time ◽  
Electron Density ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Plasma Parameters ◽  
Plasma Nitridation

We present a non-invasive approach for monitoring plasma parameters such as the electron temperature and density inside a radio-frequency (RF) plasma nitridation device using optical emission spectroscopy (OES) in conjunction with multivariate data analysis. Instead of relying on a theoretical model of the plasma emission to extract plasma parameters from the OES, an empirical correlation was established on the basis of simultaneous OES and other diagnostics. Additionally, we developed a machine learning (ML)-based virtual metrology model for real-time Te and ne monitoring in plasma nitridation processes using an in situ OES sensor. The results showed that the prediction accuracy of electron density was 97% and that of electron temperature was 90%. This method is especially useful in plasma processing because it provides in-situ and real-time analysis without disturbing the plasma or interfering with the process.

Correlation Between In Situ Optical Emission Spectroscopy in a Reactive O2 / AR RF Magnetron Sputtering Discharge and PZT Thin Film Composition

1997 ◽  
Vol 493 ◽  
Author(s):  
F. Ayguavives ◽  
P. Aubert ◽  
B. Ea-Kim ◽  
B. Agius
Keyword(s):  
Thin Film ◽  
Magnetron Sputtering ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Rf Magnetron Sputtering ◽  
Nuclear Reaction Analysis ◽  
Nominal Composition ◽  
Film Composition

ABSTRACTLead zirconate titanate (PZT) thin films have been grown by rf magnetron sputtering on Si substrates from a metallic target of nominal composition Pb1.1(Zr0.4 Ti0.6 in a reactive argon / oxygen gas mixture. During plasma deposition, in situ Optical Emission Spectroscopy (OES) measurements show clearly a correlation between the evolution of characteristic atomic emission line intensities (Zr - 386.4 nm, Ti - 399.9 nm, Pb - 405.8 nm and O - 777.2 nm) and the thin-film composition determined by a simultaneous use of Rutherford Backscattering Spectroscopy (RBS) and Nuclear Reaction Analysis (NRA).

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

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4445
Author(s):  
Yu-Pu Yang ◽  
Te-Yun Lu ◽  
Hsiao-Han Lo ◽  
Wei-Lun Chen ◽  
Peter J. Wang ◽  
...  
Keyword(s):  
Thin Film ◽  
Machine Learning ◽  
Electron Microscope ◽  
Aluminum Nitride ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Complex Data ◽  
Machine Learning Classification

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.

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