The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination
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2020
Authors
Mitić, Vojislav V.Lazović, Goran

Ribar, Srđan
Lu, Chun-An
Radović, Ivana
Stajčić, Aleksandar

Fecht, Hans

Vlahović, Branislav

Article (Published version)

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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 networkSource:
Integrated Ferroelectrics, 2020, 212, 1, 135-146Publisher:
- Taylor & Francis
DOI: 10.1080/10584587.2020.1819042
ISBN: 1058-4587
WoS: 000589431800013
Scopus: 2-s2.0-85095932312
Institution/Community
Институт техничких наука САНУ / Institute of Technical Sciences of SASATY - 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 .