The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination
Само за регистроване кориснике
2020
Аутори
Mitić, Vojislav V.Lazović, Goran
Ribar, Srđan
Lu, Chun-An
Radović, Ivana
Stajcić, Aleksandar
Fecht, Hans
Vlahović, Branislav
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
neural network / microintergranular capacity / Intergranular microelectronics / electronic signal / computing technologyИзвор:
Integrated Ferroelectrics, 2020, 212, 1, 135-146Издавач:
- Taylor & Francis Ltd, Abingdon
DOI: 10.1080/10584587.2020.1819042
ISSN: 1058-4587
WoS: 000589431800013
Scopus: 2-s2.0-85095932312
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Mitić, Vojislav V. AU - Lazović, Goran AU - Ribar, Srđan AU - Lu, Chun-An AU - Radović, Ivana AU - Stajcić, Aleksandar AU - Fecht, Hans AU - Vlahović, Branislav PY - 2020 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3426 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 Ltd, Abingdon T2 - Integrated Ferroelectrics T1 - The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination EP - 146 IS - 1 SP - 135 VL - 212 DO - 10.1080/10584587.2020.1819042 ER -
@article{ author = "Mitić, Vojislav V. and Lazović, Goran and Ribar, Srđan and Lu, Chun-An and Radović, Ivana and Stajcić, 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 Ltd, Abingdon", journal = "Integrated Ferroelectrics", title = "The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination", pages = "146-135", number = "1", volume = "212", doi = "10.1080/10584587.2020.1819042" }
Mitić, V. V., Lazović, G., Ribar, S., Lu, C., Radović, I., Stajcić, A., Fecht, H.,& Vlahović, B.. (2020). The Artificial Neural Networks Applied for Microelectronics Intergranular Relations Determination. in Integrated Ferroelectrics Taylor & Francis Ltd, Abingdon., 212(1), 135-146. https://doi.org/10.1080/10584587.2020.1819042
Mitić VV, Lazović G, Ribar S, Lu C, Radović I, Stajcić 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 .
Mitić, Vojislav V., Lazović, Goran, Ribar, Srđan, Lu, Chun-An, Radović, Ivana, Stajcić, 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 . .