Ćojbašić, Žarko М.

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  • Ćojbašić, Žarko М. (3)
  • Ćojbašić, Žarko M. (1)
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Author's Bibliography

Inverse problem solving in semiconductor photoacoustics by neural networks

Đorđević, Katarina Lj.; Markushev, Dragan D.; Ćojbašić, Žarko M.; Galović, Slobodanka

(2021)

TY  - JOUR
AU  - Đorđević, Katarina Lj.
AU  - Markushev, Dragan D.
AU  - Ćojbašić, Žarko M.
AU  - Galović, Slobodanka
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/9075
AB  - We developed a method of inverse problem solving in semiconductor photoacoustics based on neural networks application. Simple structured neural networks, trained on a large set of data obtained by the well–known theoretical models in the 20 Hz–20 kHz modulation frequency range, are applied to determine thermal diffusivity, coefficient of linear expansion and thickness of n–type silicon samples, using undistorted experimental photoacoustic signals. The efficiency of the neural networks was tested depending on the type of input data, showing the best performances in the case when signal amplitudes and phases are simultaneously presented to the network. Real–time parameter prediction is achieved together with high accuracy and reliability allowing one to perform the full characterization of a sample in the frequency domain.
T2  - Inverse Problems in Science and Engineering
T1  - Inverse problem solving in semiconductor photoacoustics by neural networks
VL  - 29
IS  - 2
SP  - 248
EP  - 262
DO  - 10.1080/17415977.2020.1787405
ER  - 
@article{
author = "Đorđević, Katarina Lj. and Markushev, Dragan D. and Ćojbašić, Žarko M. and Galović, Slobodanka",
year = "2021",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/9075",
abstract = "We developed a method of inverse problem solving in semiconductor photoacoustics based on neural networks application. Simple structured neural networks, trained on a large set of data obtained by the well–known theoretical models in the 20 Hz–20 kHz modulation frequency range, are applied to determine thermal diffusivity, coefficient of linear expansion and thickness of n–type silicon samples, using undistorted experimental photoacoustic signals. The efficiency of the neural networks was tested depending on the type of input data, showing the best performances in the case when signal amplitudes and phases are simultaneously presented to the network. Real–time parameter prediction is achieved together with high accuracy and reliability allowing one to perform the full characterization of a sample in the frequency domain.",
journal = "Inverse Problems in Science and Engineering",
title = "Inverse problem solving in semiconductor photoacoustics by neural networks",
volume = "29",
number = "2",
pages = "248-262",
doi = "10.1080/17415977.2020.1787405"
}
Đorđević, K. Lj., Markushev, D. D., Ćojbašić, Ž. M.,& Galović, S. (2021). Inverse problem solving in semiconductor photoacoustics by neural networks.
Inverse Problems in Science and Engineering, 29(2), 248-262.
https://doi.org/10.1080/17415977.2020.1787405
Đorđević KL, Markushev DD, Ćojbašić ŽM, Galović S. Inverse problem solving in semiconductor photoacoustics by neural networks. Inverse Problems in Science and Engineering. 2021;29(2):248-262
Đorđević Katarina Lj., Markushev Dragan D., Ćojbašić Žarko M., Galović Slobodanka, "Inverse problem solving in semiconductor photoacoustics by neural networks" 29, no. 2 (2021):248-262,
https://doi.org/10.1080/17415977.2020.1787405 .

Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks

Đorđević, Katarina Lj.; Markushev, Dragan D.; Ćojbašić, Žarko М.

(2020)

TY  - JOUR
AU  - Đorđević, Katarina Lj.
AU  - Markushev, Dragan D.
AU  - Ćojbašić, Žarko М.
PY  - 2020
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/8759
AB  - In this paper, a simple multilayer perceptron neural network with forward signal propagation was designed and used to simultaneously determine the main physical parameters, such as: the thermal diffusivity, thermal expansion coefficient and thickness, from the transmission, frequency-modulated photoacoustic response of the sample. The amplitude and phase responses of the transmission open-cell photoacoustic signals were calculated in n-type silicon plates using a theoretical model and were used to train and test a neural network. The simulation was done in the modulation frequency range from 20 Hz to 20 kHz and using a wide range of expected values of thermal diffusivity and the thermal coefficient of expansion for semiconductor samples as well as their thickness. The advantages and disadvantages of neural networks utilization as an appropriate mathematical tool designated for semiconductor measurement-oriented purposes are analyzed. Network reliability, precision, and the possibility of operation in real time have been verified on an independent set of signals, establishing photoacoustics as a competitive and powerful technique assigned for material characterization. © 2019, Springer Nature B.V.
T2  - Silicon
T1  - Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks
VL  - 12
IS  - 6
SP  - 1289
EP  - 1300
DO  - 10.1007/s12633-019-00213-6
ER  - 
@article{
author = "Đorđević, Katarina Lj. and Markushev, Dragan D. and Ćojbašić, Žarko М.",
year = "2020",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/8759",
abstract = "In this paper, a simple multilayer perceptron neural network with forward signal propagation was designed and used to simultaneously determine the main physical parameters, such as: the thermal diffusivity, thermal expansion coefficient and thickness, from the transmission, frequency-modulated photoacoustic response of the sample. The amplitude and phase responses of the transmission open-cell photoacoustic signals were calculated in n-type silicon plates using a theoretical model and were used to train and test a neural network. The simulation was done in the modulation frequency range from 20 Hz to 20 kHz and using a wide range of expected values of thermal diffusivity and the thermal coefficient of expansion for semiconductor samples as well as their thickness. The advantages and disadvantages of neural networks utilization as an appropriate mathematical tool designated for semiconductor measurement-oriented purposes are analyzed. Network reliability, precision, and the possibility of operation in real time have been verified on an independent set of signals, establishing photoacoustics as a competitive and powerful technique assigned for material characterization. © 2019, Springer Nature B.V.",
journal = "Silicon",
title = "Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks",
volume = "12",
number = "6",
pages = "1289-1300",
doi = "10.1007/s12633-019-00213-6"
}
Đorđević, K. Lj., Markushev, D. D.,& Ćojbašić, Ž. М. (2020). Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks.
Silicon, 12(6), 1289-1300.
https://doi.org/10.1007/s12633-019-00213-6
Đorđević KL, Markushev DD, Ćojbašić ŽМ. Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks. Silicon. 2020;12(6):1289-1300
Đorđević Katarina Lj., Markushev Dragan D., Ćojbašić Žarko М., "Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks" 12, no. 6 (2020):1289-1300,
https://doi.org/10.1007/s12633-019-00213-6 .
4
4
4

Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure

Đorđević, Katarina Lj.; Galović, Slobodanka; Jordović-Pavlović, Miroslava I.; Nešić, Mioljub V.; Popović, Marica N.; Ćojbašić, Žarko М.; Markushev, Dragan D.

(2020)

TY  - 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",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8972",
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.
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. Optical and Quantum Electronics. 2020;52(5):247
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" 52, no. 5 (2020):247,
https://doi.org/10.1007/s11082-020-02373-x .

The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain

Jordović-Pavlović, Miroslava I.; Stanković, Milena M.; Popović, Marica N.; Ćojbašić, Žarko М.; Galović, Slobodanka; Markushev, Dragan D.

(2020)

TY  - JOUR
AU  - Jordović-Pavlović, Miroslava I.
AU  - Stanković, Milena M.
AU  - Popović, Marica N.
AU  - Ćojbašić, Žarko М.
AU  - Galović, Slobodanka
AU  - Markushev, Dragan D.
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/9005
AB  - An analysis of the application of neural networks as a reliable, precise, and fast tool in open-cell photoacoustics setups for the recognition of microphone effects in the frequency domain from 10 Hz to 100 × 104 Hz is presented. The network is trained to achieve simultaneous recognition of microphone characteristics, which are the most important parameters leading to the distortion of photoacoustic signals in both amplitude and phase. The training is carried out using a theoretically obtained database of amplitudes and phases as the input and five microphone characteristics as the output, based on transmission measurements obtained using an open photoacoustic cell setup. The results show that the network can precisely and reliably interpolate the output to recognize microphone characteristics including electronic effects in the low and acoustic effects in the high frequency domain. The simulations reveal that the network is not capable of interpolating an input including modulation frequencies. Consequently, in real applications, the network training must be adapted to the experimental frequencies, or vice versa. The total number of frequencies used in the experiment must also be in accordance with the total number of frequencies used in the network training.
T2  - Journal of Computational Electronics
T1  - The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain
VL  - 19
IS  - 3
SP  - 1268
EP  - 1280
DO  - 10.1007/s10825-020-01507-4
ER  - 
@article{
author = "Jordović-Pavlović, Miroslava I. and Stanković, Milena M. and Popović, Marica N. and Ćojbašić, Žarko М. and Galović, Slobodanka and Markushev, Dragan D.",
year = "2020",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/9005",
abstract = "An analysis of the application of neural networks as a reliable, precise, and fast tool in open-cell photoacoustics setups for the recognition of microphone effects in the frequency domain from 10 Hz to 100 × 104 Hz is presented. The network is trained to achieve simultaneous recognition of microphone characteristics, which are the most important parameters leading to the distortion of photoacoustic signals in both amplitude and phase. The training is carried out using a theoretically obtained database of amplitudes and phases as the input and five microphone characteristics as the output, based on transmission measurements obtained using an open photoacoustic cell setup. The results show that the network can precisely and reliably interpolate the output to recognize microphone characteristics including electronic effects in the low and acoustic effects in the high frequency domain. The simulations reveal that the network is not capable of interpolating an input including modulation frequencies. Consequently, in real applications, the network training must be adapted to the experimental frequencies, or vice versa. The total number of frequencies used in the experiment must also be in accordance with the total number of frequencies used in the network training.",
journal = "Journal of Computational Electronics",
title = "The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain",
volume = "19",
number = "3",
pages = "1268-1280",
doi = "10.1007/s10825-020-01507-4"
}
Jordović-Pavlović, M. I., Stanković, M. M., Popović, M. N., Ćojbašić, Ž. М., Galović, S.,& Markushev, D. D. (2020). The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain.
Journal of Computational Electronics, 19(3), 1268-1280.
https://doi.org/10.1007/s10825-020-01507-4
Jordović-Pavlović MI, Stanković MM, Popović MN, Ćojbašić ŽМ, Galović S, Markushev DD. The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain. Journal of Computational Electronics. 2020;19(3):1268-1280
Jordović-Pavlović Miroslava I., Stanković Milena M., Popović Marica N., Ćojbašić Žarko М., Galović Slobodanka, Markushev Dragan D., "The application of artificial neural networks in solid-state photoacoustics for the recognition of microphone response effects in the frequency domain" 19, no. 3 (2020):1268-1280,
https://doi.org/10.1007/s10825-020-01507-4 .