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The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination

Authorized Users Only
2020
Authors
Mitić, Vojislav V.
Lazović, Goran
Ribar, Srđan
Lu, Chun-An
Radović, Ivana
Stajčić, Aleksandar
Fecht, Hans
Vlahović, Branislav
Article (Published version)
Metadata
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Abstract
This paper is based on fundamental research to develop the interface structure around the grains and to control the layers between two grains, as a prospective media for high-level electronic parameters integrations. We performed the experiments based on nano-BaTiO3 powders with Y additives. All results on dielectric parameters on submicron level are the part of global values the same measured characteristics at the bulk samples. The original idea is to develop the new computing ways to network electronic parameters in thin layers between the grains on the way to get and to compare the values on the samples. Artificial neural networks are computing tools that map input-output data and could be applied on ceramic electronic parameters. These are developed in the manner signals are processed in biological neural networks. The signals are processed by using elements which represent artificial neurons, which have a simple function to process input signal, as well as adjustable parameter wh...ich has an influence to change output signal. The total network output presents the sum of a large number neurons outputs. This important research idea is to connect analysis results and neural networks. There is a great interest to connect all of these microcapacitances by neural network with the goal to compare the results in the standard bulk samples measurements frame and microelectronics parameters. The final result of the study was functional relation definition between consolidation parameters, voltage (U) and relative capacitance change, from the level of the bulk sample down to the grains boundaries.

Keywords:
computing technology / electronic signal / intergranular microelectronics / microintergranular capacity / neural network
Source:
Integrated Ferroelectrics, 2020, 212, 1, 135-146
Publisher:
  • Taylor & Francis

DOI: 10.1080/10584587.2020.1819042

ISBN: 1058-4587

WoS: 000589431800013

Scopus: 2-s2.0-85095932312
[ Google Scholar ]
7
4
Handle
https://hdl.handle.net/21.15107/rcub_dais_9542
URI
https://dais.sanu.ac.rs/123456789/9542
Collections
  • ИТН САНУ - Општа колекција / ITS SASA - General collection
Institution/Community
Институт техничких наука САНУ / Institute of Technical Sciences of SASA
TY  - JOUR
AU  - Mitić, Vojislav V.
AU  - Lazović, Goran
AU  - Ribar, Srđan
AU  - Lu, Chun-An
AU  - Radović, Ivana
AU  - Stajčić, Aleksandar
AU  - Fecht, Hans
AU  - Vlahović, Branislav
PY  - 2020
UR  - https://dais.sanu.ac.rs/123456789/9542
AB  - This paper is based on fundamental research to develop the interface structure around the grains and to control the layers between two grains, as a prospective media for high-level electronic parameters integrations. We performed the experiments based on nano-BaTiO3 powders with Y additives. All results on dielectric parameters on submicron level are the part of global values the same measured characteristics at the bulk samples. The original idea is to develop the new computing ways to network electronic parameters in thin layers between the grains on the way to get and to compare the values on the samples. Artificial neural networks are computing tools that map input-output data and could be applied on ceramic electronic parameters. These are developed in the manner signals are processed in biological neural networks. The signals are processed by using elements which represent artificial neurons, which have a simple function to process input signal, as well as adjustable parameter which has an influence to change output signal. The total network output presents the sum of a large number neurons outputs. This important research idea is to connect analysis results and neural networks. There is a great interest to connect all of these microcapacitances by neural network with the goal to compare the results in the standard bulk samples measurements frame and microelectronics parameters. The final result of the study was functional relation definition between consolidation parameters, voltage (U) and relative capacitance change, from the level of the bulk sample down to the grains boundaries.
PB  - Taylor & Francis
T2  - Integrated Ferroelectrics
T1  - The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination
SP  - 135
EP  - 146
VL  - 212
IS  - 1
DO  - 10.1080/10584587.2020.1819042
UR  - https://hdl.handle.net/21.15107/rcub_dais_9542
ER  - 
@article{
author = "Mitić, Vojislav V. and Lazović, Goran and Ribar, Srđan and Lu, Chun-An and Radović, Ivana and Stajčić, Aleksandar and Fecht, Hans and Vlahović, Branislav",
year = "2020",
abstract = "This paper is based on fundamental research to develop the interface structure around the grains and to control the layers between two grains, as a prospective media for high-level electronic parameters integrations. We performed the experiments based on nano-BaTiO3 powders with Y additives. All results on dielectric parameters on submicron level are the part of global values the same measured characteristics at the bulk samples. The original idea is to develop the new computing ways to network electronic parameters in thin layers between the grains on the way to get and to compare the values on the samples. Artificial neural networks are computing tools that map input-output data and could be applied on ceramic electronic parameters. These are developed in the manner signals are processed in biological neural networks. The signals are processed by using elements which represent artificial neurons, which have a simple function to process input signal, as well as adjustable parameter which has an influence to change output signal. The total network output presents the sum of a large number neurons outputs. This important research idea is to connect analysis results and neural networks. There is a great interest to connect all of these microcapacitances by neural network with the goal to compare the results in the standard bulk samples measurements frame and microelectronics parameters. The final result of the study was functional relation definition between consolidation parameters, voltage (U) and relative capacitance change, from the level of the bulk sample down to the grains boundaries.",
publisher = "Taylor & Francis",
journal = "Integrated Ferroelectrics",
title = "The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination",
pages = "135-146",
volume = "212",
number = "1",
doi = "10.1080/10584587.2020.1819042",
url = "https://hdl.handle.net/21.15107/rcub_dais_9542"
}
Mitić, V. V., Lazović, G., Ribar, S., Lu, C., Radović, I., Stajčić, A., Fecht, H.,& Vlahović, B.. (2020). The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination. in Integrated Ferroelectrics
Taylor & Francis., 212(1), 135-146.
https://doi.org/10.1080/10584587.2020.1819042
https://hdl.handle.net/21.15107/rcub_dais_9542
Mitić VV, Lazović G, Ribar S, Lu C, Radović I, Stajčić A, Fecht H, Vlahović B. The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination. in Integrated Ferroelectrics. 2020;212(1):135-146.
doi:10.1080/10584587.2020.1819042
https://hdl.handle.net/21.15107/rcub_dais_9542 .
Mitić, Vojislav V., Lazović, Goran, Ribar, Srđan, Lu, Chun-An, Radović, Ivana, Stajčić, Aleksandar, Fecht, Hans, Vlahović, Branislav, "The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination" in Integrated Ferroelectrics, 212, no. 1 (2020):135-146,
https://doi.org/10.1080/10584587.2020.1819042 .,
https://hdl.handle.net/21.15107/rcub_dais_9542 .

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