TechnoRep - Репозиторијум Технолошко-металуршког факултета
Универзитет у Београду, Технолошко-металуршки факултет
    • English
    • Српски
    • Српски (Serbia)
  • Српски (ћирилица) 
    • Енглески
    • Српски (ћирилица)
    • Српски (латиница)
  • Пријава
Преглед записа 
  •   TechnoRep
  • Tehnološko-metalurški fakultet
  • Radovi istraživača / Researchers’ publications (TMF)
  • Преглед записа
  •   TechnoRep
  • Tehnološko-metalurški fakultet
  • Radovi istraživača / Researchers’ publications (TMF)
  • Преглед записа
JavaScript is disabled for your browser. Some features of this site may not work without it.

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Roughness of Cu coatings

Thumbnail
2020
4417.pdf (1.177Mb)
Аутори
Mladenović, Ivana
Lamovec, Jelena
Nikolić, Nebojša
Andrić, Stevan
Obradov, Marko
Radojević, Vesna
Vasiljević-Radović, Dana
conferenceObject (publishedVersion)
Метаподаци
Приказ свих података о документу
Апстракт
Copper coatings are produced on silicon wafer by electrodeposition (ED) in pulsating current (PC) regime. Electrodeposition was performed at various current density amplitudes in the range of 80−140 mA cm-2, frequency in the range of 30−100 Hz and coating thickness in the range of 10−60 μm. The resulting composite systems consist of monolayered copper films electrodeposited from sulfate bath on Si wafers with sputtered layers of Cr/Au. Roughness measurements were performed to evaluate properties of the copper coating surface. The coating roughness (R) was measured using Atomic Force Microscope in contact mode. The software Gwyddion was used for determination an average roughness parameter (Ra). After that (Artificial Neural Network-ANN) model was used to study the relationship between the parameters of electrodeposition process and roughness of copper coatings. The influence of experimental values: amplitude current density, frequency and thickness of coating on the surface roughness w...ill be highlighted. Response surface methodology (RSM) was utilized to improve the correction between Ra and input parameters. Finally, the results of the average roughness (experimental and predicted) were used to estimate the new value of (Ra) of copper for each variation of the input parameters and compared capability of ANN and regression analysis for surface roughness generated under different electrochemical conditions. The coefficient of determination was found 92% for ANN and 93% for regression analysis.

Кључне речи:
electrodeposition / electrodeposition / roughness / AFM / coatings / ANN / RSM / Artificial Neural Network / models / prediction / roughness / AFM / coatings / ANN / RSM.
Извор:
Proceedings - 7th International Conference on Electrical, Electronic and Computing Engineering IcETR, 2020
Издавач:
  • Belgrade: ETRAN – Society for electronics, telecommunication, computing, automatics and nuclear angineering
Финансирање / пројекти:
  • info:eu-repo/grantAgreement/MESTD/inst-2020/200026/RS// (RS-200026)
  • info:eu-repo/grantAgreement/MESTD/inst-2020/200135/RS// (RS-200135)
[ Google Scholar ]
URI
http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4420
Колекције
  • Radovi istraživača / Researchers’ publications (TMF)
Институција/група
Tehnološko-metalurški fakultet

DSpace software copyright © 2002-2015  DuraSpace
О репозиторијуму TechnoRep | Пошаљите запажања

OpenAIRERCUB
 

 

Комплетан репозиторијумИнституције/групеАуториНасловиТемеОва институцијаАуториНасловиТеме

Статистика

Преглед статистика

DSpace software copyright © 2002-2015  DuraSpace
О репозиторијуму TechnoRep | Пошаљите запажања

OpenAIRERCUB