CER - Central Repository
Institute of Chemistry, Technology and Metallurgy
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrillic)
    • Serbian (Latin)
  • Login
View Item 
  •   CER
  • IHTM
  • Radovi istraživača / Researchers' publications
  • View Item
  •   CER
  • IHTM
  • Radovi istraživača / Researchers' publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Neural networks and microelectronics parameters distribution measurements depending on sintering temperature and applied voltage

Authorized Users Only
2020
Authors
Mitić, Vojislav V.
Ribar, Srđan
Randjelović, Branislav M.
Lu, Chunan
Radović, Ivana
Stajčić, Aleksandar
Novaković, Igor
Vlahović, Branislav
Article (Published version)
Metadata
Show full item record
Abstract
This research is based on the idea to design the interface structure around the grains and thin layers between two grains, as a possible solution for deep microelectronic parameters integrations. The experiments have been based on nano-BaTiO3 powders with Y-based additive. The advanced idea is to create the new observed directions to network microelectronic characteristics in thin films coated around and between the grains on the way to get and compare with global results on the samples. Biomimetic similarities are artificial neural networks which could be original method and tools that we use to map input-output data and could be applied on ceramics microelectronic parameters. This mapping is developed in the manner like signals that are processed in real biological neural networks. These signals are processed by using artificial neurons, which have a simple function to process input signal, as well as adjustable parameter which represents sensitivity to inputs. The integrated network... output presents practically the large number of inner neurons outputs sum. This original idea is to connect analysis results and neural networks. It is of the great importance to connect microcapacitances by neural network with the goal to compare the experimental results in the bulk samples measurements and microelectronics parameters. The result of these researches is the study of functional relation definition between consolidation parameters, voltage (U), consolidation sintering temperature and relative capacitance change, from the bulk sample surface down to the coating thin films around the grains.

Keywords:
BaTiO3 / intergranular capacity / Neural network / supervised learning
Source:
Modern Physics Letters B, 2020, 34, 35, 2150172-
Publisher:
  • World Scientific

DOI: 10.1142/S0217984921501724

ISSN: 0217-9849; 1793-6640

WoS: 000603067600014

Scopus: 2-s2.0-85098142433
[ Google Scholar ]
10
4
URI
https://cer.ihtm.bg.ac.rs/handle/123456789/4242
Collections
  • Radovi istraživača / Researchers' publications
Institution/Community
IHTM
TY  - JOUR
AU  - Mitić, Vojislav V.
AU  - Ribar, Srđan
AU  - Randjelović, Branislav M.
AU  - Lu, Chunan
AU  - Radović, Ivana
AU  - Stajčić, Aleksandar
AU  - Novaković, Igor
AU  - Vlahović, Branislav
PY  - 2020
UR  - https://cer.ihtm.bg.ac.rs/handle/123456789/4242
AB  - This research is based on the idea to design the interface structure around the grains and thin layers between two grains, as a possible solution for deep microelectronic parameters integrations. The experiments have been based on nano-BaTiO3 powders with Y-based additive. The advanced idea is to create the new observed directions to network microelectronic characteristics in thin films coated around and between the grains on the way to get and compare with global results on the samples. Biomimetic similarities are artificial neural networks which could be original method and tools that we use to map input-output data and could be applied on ceramics microelectronic parameters. This mapping is developed in the manner like signals that are processed in real biological neural networks. These signals are processed by using artificial neurons, which have a simple function to process input signal, as well as adjustable parameter which represents sensitivity to inputs. The integrated network output presents practically the large number of inner neurons outputs sum. This original idea is to connect analysis results and neural networks. It is of the great importance to connect microcapacitances by neural network with the goal to compare the experimental results in the bulk samples measurements and microelectronics parameters. The result of these researches is the study of functional relation definition between consolidation parameters, voltage (U), consolidation sintering temperature and relative capacitance change, from the bulk sample surface down to the coating thin films around the grains.
PB  - World Scientific
T2  - Modern Physics Letters B
T1  - Neural networks and microelectronics parameters distribution measurements depending on sintering temperature and applied voltage
VL  - 34
IS  - 35
SP  - 2150172
DO  - 10.1142/S0217984921501724
ER  - 
@article{
author = "Mitić, Vojislav V. and Ribar, Srđan and Randjelović, Branislav M. and Lu, Chunan and Radović, Ivana and Stajčić, Aleksandar and Novaković, Igor and Vlahović, Branislav",
year = "2020",
abstract = "This research is based on the idea to design the interface structure around the grains and thin layers between two grains, as a possible solution for deep microelectronic parameters integrations. The experiments have been based on nano-BaTiO3 powders with Y-based additive. The advanced idea is to create the new observed directions to network microelectronic characteristics in thin films coated around and between the grains on the way to get and compare with global results on the samples. Biomimetic similarities are artificial neural networks which could be original method and tools that we use to map input-output data and could be applied on ceramics microelectronic parameters. This mapping is developed in the manner like signals that are processed in real biological neural networks. These signals are processed by using artificial neurons, which have a simple function to process input signal, as well as adjustable parameter which represents sensitivity to inputs. The integrated network output presents practically the large number of inner neurons outputs sum. This original idea is to connect analysis results and neural networks. It is of the great importance to connect microcapacitances by neural network with the goal to compare the experimental results in the bulk samples measurements and microelectronics parameters. The result of these researches is the study of functional relation definition between consolidation parameters, voltage (U), consolidation sintering temperature and relative capacitance change, from the bulk sample surface down to the coating thin films around the grains.",
publisher = "World Scientific",
journal = "Modern Physics Letters B",
title = "Neural networks and microelectronics parameters distribution measurements depending on sintering temperature and applied voltage",
volume = "34",
number = "35",
pages = "2150172",
doi = "10.1142/S0217984921501724"
}
Mitić, V. V., Ribar, S., Randjelović, B. M., Lu, C., Radović, I., Stajčić, A., Novaković, I.,& Vlahović, B.. (2020). Neural networks and microelectronics parameters distribution measurements depending on sintering temperature and applied voltage. in Modern Physics Letters B
World Scientific., 34(35), 2150172.
https://doi.org/10.1142/S0217984921501724
Mitić VV, Ribar S, Randjelović BM, Lu C, Radović I, Stajčić A, Novaković I, Vlahović B. Neural networks and microelectronics parameters distribution measurements depending on sintering temperature and applied voltage. in Modern Physics Letters B. 2020;34(35):2150172.
doi:10.1142/S0217984921501724 .
Mitić, Vojislav V., Ribar, Srđan, Randjelović, Branislav M., Lu, Chunan, Radović, Ivana, Stajčić, Aleksandar, Novaković, Igor, Vlahović, Branislav, "Neural networks and microelectronics parameters distribution measurements depending on sintering temperature and applied voltage" in Modern Physics Letters B, 34, no. 35 (2020):2150172,
https://doi.org/10.1142/S0217984921501724 . .

DSpace software copyright © 2002-2015  DuraSpace
About CeR – Central Repository | Send Feedback

re3dataOpenAIRERCUB
 

 

All of DSpaceInstitutions/communitiesAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About CeR – Central Repository | Send Feedback

re3dataOpenAIRERCUB