scholarly journals Study on the properties and optical emission spectroscopy of the intrinsic silicon thin film in silicon heterojunction solar cells

2013 ◽  
Vol 62 (19) ◽  
pp. 197301
Author(s):  
Xue Yuan ◽  
Gao Chao-Jun ◽  
Gu Jin-Hua ◽  
Feng Ya-Yang ◽  
Yang Shi-E ◽  
...  
Keyword(s):  
Thin Film ◽  
Solar Cells ◽  
Emission Spectroscopy ◽  
Optical Emission Spectroscopy ◽  
Optical Emission ◽  
Silicon Thin Film ◽  
Heterojunction Solar Cells ◽  
Silicon Heterojunction Solar Cells

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 Amorphous-silicon thin-film heterojunction solar cells

1981 ◽  
Author(s):  
M. C. Cretella ◽  
J. A. Gregory ◽  
D. B. Sandstrom ◽  
W. Paul
Keyword(s):  
Thin Film ◽  
Solar Cells ◽  
Amorphous Silicon ◽  
Silicon Thin Film ◽  
Heterojunction Solar Cells

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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