TY  - CONF
AU  - Đorđević, Katarina Lj.
AU  - Galović, Slobodanka
AU  - Ćojbašić, Ž. M.
AU  - Markushev, Dragan D.
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10917
AB  - In this paper, electronic semiconductor characterization using reverse-back procedure was applied to different photoacoustic (PA) responses aiming to find effective ambipolar diffusion coefficient and a bulk life-time of the minority carriers. The main idea was to find the small fluctuations in investigated parameters due to detecting possible unwanted sample contaminations and temperature variations during the measurements. The mentioned procedure was based on the application of neural networks [1]. Knowing that in experiments the contaminated surfaces of the sample can play a significant role in the global recombination process that we are measuring and that the unintentionally introduced defects of the sample crystal lattice could vary the carrier lifetime by several orders of magnitude, a method of PA signal adjustment by the reverse-back procedure is developed, based on the changes of the carrier electronic parameters.Such changes are detected (Fig.1) and calculated here by analyzing PA signal amplitude ratios A ANN / A expand phase differences ΦANN – Φexpobtainedusing experimental (exp) and network predicted (ANN) thermal and geometrical parameters of the sample [2].The values of photogenerated carrier lifetime and ambipolar diffusion coefficient obtained by the presented method can be used in the quality control procedure of the investigated samples, active control of the experimental conditions and within the general characterization process of semiconductors.
PB  - Belgrade : Institute of Physics Belgrade
C3  - PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade
T1  - Electronic Semiconductor Characterization Using Reverse-Back Procedure Based on Neural Networks and Photoacoustic Response
SP  - 169
UR  - https://hdl.handle.net/21.15107/rcub_vinar_10917
ER  - 

@conference{
author = "Đorđević, Katarina Lj. and Galović, Slobodanka and Ćojbašić, Ž. M. and Markushev, Dragan D.",
year = "2021",
abstract = "In this paper, electronic semiconductor characterization using reverse-back procedure was applied to different photoacoustic (PA) responses aiming to find effective ambipolar diffusion coefficient and a bulk life-time of the minority carriers. The main idea was to find the small fluctuations in investigated parameters due to detecting possible unwanted sample contaminations and temperature variations during the measurements. The mentioned procedure was based on the application of neural networks [1]. Knowing that in experiments the contaminated surfaces of the sample can play a significant role in the global recombination process that we are measuring and that the unintentionally introduced defects of the sample crystal lattice could vary the carrier lifetime by several orders of magnitude, a method of PA signal adjustment by the reverse-back procedure is developed, based on the changes of the carrier electronic parameters.Such changes are detected (Fig.1) and calculated here by analyzing PA signal amplitude ratios A ANN / A expand phase differences ΦANN – Φexpobtainedusing experimental (exp) and network predicted (ANN) thermal and geometrical parameters of the sample [2].The values of photogenerated carrier lifetime and ambipolar diffusion coefficient obtained by the presented method can be used in the quality control procedure of the investigated samples, active control of the experimental conditions and within the general characterization process of semiconductors.",
publisher = "Belgrade : Institute of Physics Belgrade",
journal = "PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade",
title = "Electronic Semiconductor Characterization Using Reverse-Back Procedure Based on Neural Networks and Photoacoustic Response",
pages = "169",
url = "https://hdl.handle.net/21.15107/rcub_vinar_10917"
}

Đorđević, K. Lj., Galović, S., Ćojbašić, Ž. M.,& Markushev, D. D.. (2021). Electronic Semiconductor Characterization Using Reverse-Back Procedure Based on Neural Networks and Photoacoustic Response. in PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade
Belgrade : Institute of Physics Belgrade., 169.
https://hdl.handle.net/21.15107/rcub_vinar_10917

Đorđević KL, Galović S, Ćojbašić ŽM, Markushev DD. Electronic Semiconductor Characterization Using Reverse-Back Procedure Based on Neural Networks and Photoacoustic Response. in PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade. 2021;:169.
https://hdl.handle.net/21.15107/rcub_vinar_10917 .

Đorđević, Katarina Lj., Galović, Slobodanka, Ćojbašić, Ž. M., Markushev, Dragan D., "Electronic Semiconductor Characterization Using Reverse-Back Procedure Based on Neural Networks and Photoacoustic Response" in PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade (2021):169,
https://hdl.handle.net/21.15107/rcub_vinar_10917 .

TY  - CONF
AU  - Đorđević, Katarina Lj.
AU  - Jordović-Pavlović, Miroslava I.
AU  - Ćojbašić, Ž. M.
AU  - Galović, Slobodanka
AU  - Popović, Marica N.
AU  - Nešić, Mioljub V.
AU  - Markushev, Dragan D.
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10918
AB  - In our previous articles [1,2] we have shown that the application of artificial neural networks (ANNs) in photoacoustics could improve experimental procedures in many ways: better accuracy and precision in investigated sample parameters prediction, better control of the experimental conditions together with approaching to the real-time characterization of the investigated sample, etc. Here we will try to show why the different types of scaling and normalization procedures could be beneficial to the accuracy, precision and numerical stability of the network predicted parameters and network training speed. To do that numerical (Fig.1) or logarithmic scaling and min-max and max normalizations are applied on experimental input data used in the ANNs training process. At the same time, specific numerical scaling is used for network output data (predicted sample thermal and geometric parameters such as thermal diffusivity, linear coefficient of thermal expansion, thickness) to find possible benefits to ANNs performances. Our analysis of training, stability, and accuracy of network prediction will rely on the ANNs trained with or without scaling and/or normalization to find their influence on overall network performances.
PB  - Belgrade : Institute of Physics Belgrade
C3  - PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade
T1  - Influence of data scaling and normalization on overall neural network performances in photoacoustics
SP  - 173
UR  - https://hdl.handle.net/21.15107/rcub_vinar_10918
ER  - 

@conference{
author = "Đorđević, Katarina Lj. and Jordović-Pavlović, Miroslava I. and Ćojbašić, Ž. M. and Galović, Slobodanka and Popović, Marica N. and Nešić, Mioljub V. and Markushev, Dragan D.",
year = "2021",
abstract = "In our previous articles [1,2] we have shown that the application of artificial neural networks (ANNs) in photoacoustics could improve experimental procedures in many ways: better accuracy and precision in investigated sample parameters prediction, better control of the experimental conditions together with approaching to the real-time characterization of the investigated sample, etc. Here we will try to show why the different types of scaling and normalization procedures could be beneficial to the accuracy, precision and numerical stability of the network predicted parameters and network training speed. To do that numerical (Fig.1) or logarithmic scaling and min-max and max normalizations are applied on experimental input data used in the ANNs training process. At the same time, specific numerical scaling is used for network output data (predicted sample thermal and geometric parameters such as thermal diffusivity, linear coefficient of thermal expansion, thickness) to find possible benefits to ANNs performances. Our analysis of training, stability, and accuracy of network prediction will rely on the ANNs trained with or without scaling and/or normalization to find their influence on overall network performances.",
publisher = "Belgrade : Institute of Physics Belgrade",
journal = "PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade",
title = "Influence of data scaling and normalization on overall neural network performances in photoacoustics",
pages = "173",
url = "https://hdl.handle.net/21.15107/rcub_vinar_10918"
}

Đorđević, K. Lj., Jordović-Pavlović, M. I., Ćojbašić, Ž. M., Galović, S., Popović, M. N., Nešić, M. V.,& Markushev, D. D.. (2021). Influence of data scaling and normalization on overall neural network performances in photoacoustics. in PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade
Belgrade : Institute of Physics Belgrade., 173.
https://hdl.handle.net/21.15107/rcub_vinar_10918

Đorđević KL, Jordović-Pavlović MI, Ćojbašić ŽM, Galović S, Popović MN, Nešić MV, Markushev DD. Influence of data scaling and normalization on overall neural network performances in photoacoustics. in PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade. 2021;:173.
https://hdl.handle.net/21.15107/rcub_vinar_10918 .

Đorđević, Katarina Lj., Jordović-Pavlović, Miroslava I., Ćojbašić, Ž. M., Galović, Slobodanka, Popović, Marica N., Nešić, Mioljub V., Markushev, Dragan D., "Influence of data scaling and normalization on overall neural network performances in photoacoustics" in PHOTONICA2021 : 8th International School and Conference on Photonics and HEMMAGINERO workshop : Abstracts of Tutorial, Keynote, Invited Lectures, Progress Reports and Contributed Papers; August 23-27, 2021; Belgrade (2021):173,
https://hdl.handle.net/21.15107/rcub_vinar_10918 .