Correlation between in situ optical emission spectroscopy in a reactive Ar/O2 rf magnetron sputtering discharge and Pb(ZrxTi1−x)O3 thin film composition

1998 ◽  
Vol 73 (8) ◽  
pp. 1023-1025 ◽  
Author(s):  
F. Ayguavives ◽  
B. Ea-kim ◽  
P. Aubert ◽  
B. Agius ◽  
J. Bretagne
Keyword(s):  
Thin Film ◽  
Magnetron Sputtering ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Rf Magnetron Sputtering ◽  
Film Composition

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.

Influence of Dissipation Power in Copper Sputtering Plasma Measured by Optical Emission Spectroscopy

2013 ◽  
Vol 832 ◽  
pp. 243-247
Author(s):  
Kevin Low ◽  
Nayan Nafarizal ◽  
Mohd Zainizan Sahdan ◽  
Mahamad Abd Kadir ◽  
Mohd Khairul bin Ahmad ◽  
...  
Keyword(s):  
Thin Film ◽  
Magnetron Sputtering ◽  
Copper Oxide ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Reactive Magnetron Sputtering ◽  
Oxide Thin Film ◽  
Reactive Magnetron ◽  
Copper Oxide Thin Film

Copper oxide is a low cost material, easy process fabrication and sensitivity to ambient conditions. Therefore, it is a suitable p-type semiconductor oxides material to be used as a gas sensing material. In order to raise the sensitivity of the copper oxide gas sensor, study on the correspondence in between the coated thin film with coating parameters is an important part. In current study, optical emission spectroscopy is used to investigate the reactive magnetron sputtering plasma during the deposition of copper oxide thin film. The measurement point was focused at roughly 2cm above the substrate holder. The emission of copper, oxygen and argon in the reactive magnetron sputtering were observed at various plasma conditions. In general, the emission of copper, oxygen and argon increased when the discharge rf power is increased. On the other hand, oxygen line intensity was found to be excess when the oxygen flow rate is above 8sccm. The result suggests the best condition to deposit the copper oxide thin film using solid 3 copper target.

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.

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