scholarly journals The effect of DC-bias plasma oxygen on the surface chemistry of polystyrene thin film analysis by optical emission spectroscopy

2020 ◽  
Author(s):  
Putri S. Arinda ◽  
Mahardika A. Hanif ◽  
D. J. Djoko H. Sandtjojo ◽  
Setyawan P. Sakti
Keyword(s):  
Thin Film ◽  
Surface Chemistry ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Film Analysis ◽  
Thin Film Analysis ◽  
Dc Bias ◽  
Plasma Oxygen

scholarly journals Analysis of Optical Emission Spectra during Nitrogen-Plasma Treatment to Control the Wettability of Polystyrene Surface

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
Masruroh ◽  
Dionysius J. D. H. Santjojo ◽  
Ahmad Taufiq
Keyword(s):  
Plasma Treatment ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Nitrogen Plasma ◽  
Chamber Pressure ◽  
Treatment Results ◽  
Plasma Species ◽  
Dc Bias ◽  
Nitrogen Treatment

In this work, we apply optical emission spectroscopy to investigate active plasma species to study that plasma nitrogen treatment affects polystyrene surfaces. Data concerning these active plasma species are crucial for exploring the polystyrene layer's functionality deposited on quartz crystal microbalance (QCM) surface. Wettability function in biosensors development is essential aspects for biomolecule immobilization. The surface of the polystyrene layer was modified by plasma nitrogen treatment. The process parameters affecting plasma species and characteristic, and hence the treatment results studied in this work were chamber pressure, flow rate, and DC bias. The plasma analysis was conducted by optical emission spectroscopy. The spectroscopy was utilized to predict the active species of plasma, the electron temperature Te and the electron density Ne. The dominant reactive species was N2+ which go through different plasma interactions and on the polystyrene surface depending on the DC bias voltage, the nitrogen- gas flow rate, and the chamber pressure. The plasma treatment results suggest that the ion bombardment was the dominant mechanism that changes the polystyrene's surface. The plasma behavior and surface interactions were found complex with the variation of the process parameter. Keywords: Electron density, Electron temperature, OES, Nitrogen-plasma treatment, Wettability

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.

Glow discharge optical emission spectrometry: moving towards reliable thin film analysis–a short review

2003 ◽  
Vol 18 (6) ◽  
pp. 670-679 ◽  
Author(s):  
Johann Angeli ◽  
Arne Bengtson ◽  
Annemie Bogaerts ◽  
Volker Hoffmann ◽  
Vasile-Dan Hodoroaba ◽  
...  
Keyword(s):  
Thin Film ◽  
Glow Discharge ◽  
Optical Emission ◽  
Short Review ◽  
Optical Emission Spectrometry ◽  
Emission Spectrometry ◽  
Film Analysis ◽  
Thin Film Analysis
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