Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure
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2020
Authors
Đorđević, Katarina Lj.Galović, Slobodanka
Jordović-Pavlović, Miroslava I.
Nešić, Mioljub V.
Popović, Marica N.
Ćojbašić, Žarko М.
Markushev, Dragan D.
Article (Published version)
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature
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This paper introduces the possibility of the determination of optical absorption and reflexivity coefficient of silicon samples using neural networks and reverse-back procedure based on the photoacoustics response in the frequency domain. Differences between neural network predictions and parameters obtained with standard photoacoustic signal correction procedures are used to adjust our experimental set-up due to the instability of the optical excitation source and the state (contamination) of the illuminated surface. It has been shown that the changes of the optical absorption values correspond to the light source wavelength fluctuations, while changes in the reflexivity coefficient, obtained in this way, correspond to the small effect of the ultrathin layer formation of SiO2 due to the natural process of surface oxidation.
Keywords:
Photoacoustic / Semiconductors / Artificial neural networks / Thermal diffusion / Thermal expansion / Photothermal / Inverse problem / n-type silicon / Reverse-back procedureSource:
Optical and Quantum Electronics, 2020, 52, 5, 247-Funding / projects:
- Atomic collision processes and photoacoustic spectroscopy of molecules and solids (RS-MESTD-Basic Research (BR or ON)-171016)
- Functional, Functionalized and Advanced Nanomaterials (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-45005)
DOI: 10.1007/s11082-020-02373-x
ISSN: 0306-8919
WoS: 000531865400004
Scopus: 2-s2.0-85084190651
Institution/Community
VinčaTY - JOUR AU - Đorđević, Katarina Lj. AU - Galović, Slobodanka AU - Jordović-Pavlović, Miroslava I. AU - Nešić, Mioljub V. AU - Popović, Marica N. AU - Ćojbašić, Žarko М. AU - Markushev, Dragan D. PY - 2020 UR - https://vinar.vin.bg.ac.rs/handle/123456789/8972 AB - This paper introduces the possibility of the determination of optical absorption and reflexivity coefficient of silicon samples using neural networks and reverse-back procedure based on the photoacoustics response in the frequency domain. Differences between neural network predictions and parameters obtained with standard photoacoustic signal correction procedures are used to adjust our experimental set-up due to the instability of the optical excitation source and the state (contamination) of the illuminated surface. It has been shown that the changes of the optical absorption values correspond to the light source wavelength fluctuations, while changes in the reflexivity coefficient, obtained in this way, correspond to the small effect of the ultrathin layer formation of SiO2 due to the natural process of surface oxidation. T2 - Optical and Quantum Electronics T1 - Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure VL - 52 IS - 5 SP - 247 DO - 10.1007/s11082-020-02373-x ER -
@article{ author = "Đorđević, Katarina Lj. and Galović, Slobodanka and Jordović-Pavlović, Miroslava I. and Nešić, Mioljub V. and Popović, Marica N. and Ćojbašić, Žarko М. and Markushev, Dragan D.", year = "2020", abstract = "This paper introduces the possibility of the determination of optical absorption and reflexivity coefficient of silicon samples using neural networks and reverse-back procedure based on the photoacoustics response in the frequency domain. Differences between neural network predictions and parameters obtained with standard photoacoustic signal correction procedures are used to adjust our experimental set-up due to the instability of the optical excitation source and the state (contamination) of the illuminated surface. It has been shown that the changes of the optical absorption values correspond to the light source wavelength fluctuations, while changes in the reflexivity coefficient, obtained in this way, correspond to the small effect of the ultrathin layer formation of SiO2 due to the natural process of surface oxidation.", journal = "Optical and Quantum Electronics", title = "Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure", volume = "52", number = "5", pages = "247", doi = "10.1007/s11082-020-02373-x" }
Đorđević, K. Lj., Galović, S., Jordović-Pavlović, M. I., Nešić, M. V., Popović, M. N., Ćojbašić, Ž. М.,& Markushev, D. D.. (2020). Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure. in Optical and Quantum Electronics, 52(5), 247. https://doi.org/10.1007/s11082-020-02373-x
Đorđević KL, Galović S, Jordović-Pavlović MI, Nešić MV, Popović MN, Ćojbašić ŽМ, Markushev DD. Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure. in Optical and Quantum Electronics. 2020;52(5):247. doi:10.1007/s11082-020-02373-x .
Đorđević, Katarina Lj., Galović, Slobodanka, Jordović-Pavlović, Miroslava I., Nešić, Mioljub V., Popović, Marica N., Ćojbašić, Žarko М., Markushev, Dragan D., "Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure" in Optical and Quantum Electronics, 52, no. 5 (2020):247, https://doi.org/10.1007/s11082-020-02373-x . .