TY  - CONF
AU  - Mladenović, Ivana
AU  - Lamovec, Jelena
AU  - Nikolić, Nebojša
AU  - Andrić, Stevan
AU  - Obradov, Marko
AU  - Radojević, Vesna
AU  - Vasiljević-Radović, Dana
PY  - 2020
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4420
AB  - 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 will 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.
PB  - Belgrade: ETRAN – Society for electronics, telecommunication, computing, automatics and nuclear angineering
C3  - Proceedings - 7th International Conference on Electrical, Electronic and Computing Engineering IcETR
T1  - Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Roughness of Cu coatings
UR  - https://hdl.handle.net/21.15107/rcub_technorep_4420
ER  - 

@conference{
author = "Mladenović, Ivana and Lamovec, Jelena and Nikolić, Nebojša and Andrić, Stevan and Obradov, Marko and Radojević, Vesna and Vasiljević-Radović, Dana",
year = "2020",
abstract = "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 will 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.",
publisher = "Belgrade: ETRAN – Society for electronics, telecommunication, computing, automatics and nuclear angineering",
journal = "Proceedings - 7th International Conference on Electrical, Electronic and Computing Engineering IcETR",
title = "Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Roughness of Cu coatings",
url = "https://hdl.handle.net/21.15107/rcub_technorep_4420"
}

Mladenović, I., Lamovec, J., Nikolić, N., Andrić, S., Obradov, M., Radojević, V.,& Vasiljević-Radović, D.. (2020). Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Roughness of Cu coatings. in Proceedings - 7th International Conference on Electrical, Electronic and Computing Engineering IcETR
Belgrade: ETRAN – Society for electronics, telecommunication, computing, automatics and nuclear angineering..
https://hdl.handle.net/21.15107/rcub_technorep_4420

Mladenović I, Lamovec J, Nikolić N, Andrić S, Obradov M, Radojević V, Vasiljević-Radović D. Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Roughness of Cu coatings. in Proceedings - 7th International Conference on Electrical, Electronic and Computing Engineering IcETR. 2020;.
https://hdl.handle.net/21.15107/rcub_technorep_4420 .

Mladenović, Ivana, Lamovec, Jelena, Nikolić, Nebojša, Andrić, Stevan, Obradov, Marko, Radojević, Vesna, Vasiljević-Radović, Dana, "Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Roughness of Cu coatings" in Proceedings - 7th International Conference on Electrical, Electronic and Computing Engineering IcETR (2020),
https://hdl.handle.net/21.15107/rcub_technorep_4420 .

TY  - JOUR
AU  - Mladenović, Ivana
AU  - Lamovec, Jelena
AU  - Jović, Vesna
AU  - Obradov, Marko
AU  - Vasiljević-Radović, Dana
AU  - Nikolić, Nebojša
AU  - Radojević, Vesna
PY  - 2019
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4069
AB  - The mechanical properties of systems consisting of copper coatings electrodeposited on both brass sheet (BS) and thick electrodeposited nickel coating (ED Ni) substrates have been investigated. The electrodeposition of copper coatings was performed with and without the ultrasound assistance. The ultrasound application decreases root mean square (RMS) roughness of deposited Cu coating on both applied substrates, as obtained from non-contact AFM measurement. The coating roughness is highly dependent on the substrate roughness, being the smallest for the Cu coatings deposited on ED Ni substrate with the ultrasound mixing. The hardness and adhesion properties were characterized using the Vickers microindentation test. Model of Korsunsky was applied to the experimental data for determination the film hardness and the model of Chen-Gao was used for the adhesion evaluation. The introduction of ultrasonic agitation caused the changes in the film microstructure, and consequently in the mechanical properties. The copper coatings on both substrates, have higher hardness when deposited from electrolyte with ultrasound agitation. Although the type of the substrate has the major influence on the adhesion strength, it can be said that Cu electrodeposition with ultrasonic mixing contributes to an increase in adhesion.
PB  - Srpsko hemijsko društvo, Beograd
T2  - Journal of the Serbian Chemical Society
T1  - Mechanical characterization of copper coatings electrodeposited onto different substrates with and without ultrasound assistance
EP  - 741
IS  - 7
SP  - 729
VL  - 84
DO  - 10.2298/JSC181003023M
ER  - 

@article{
author = "Mladenović, Ivana and Lamovec, Jelena and Jović, Vesna and Obradov, Marko and Vasiljević-Radović, Dana and Nikolić, Nebojša and Radojević, Vesna",
year = "2019",
abstract = "The mechanical properties of systems consisting of copper coatings electrodeposited on both brass sheet (BS) and thick electrodeposited nickel coating (ED Ni) substrates have been investigated. The electrodeposition of copper coatings was performed with and without the ultrasound assistance. The ultrasound application decreases root mean square (RMS) roughness of deposited Cu coating on both applied substrates, as obtained from non-contact AFM measurement. The coating roughness is highly dependent on the substrate roughness, being the smallest for the Cu coatings deposited on ED Ni substrate with the ultrasound mixing. The hardness and adhesion properties were characterized using the Vickers microindentation test. Model of Korsunsky was applied to the experimental data for determination the film hardness and the model of Chen-Gao was used for the adhesion evaluation. The introduction of ultrasonic agitation caused the changes in the film microstructure, and consequently in the mechanical properties. The copper coatings on both substrates, have higher hardness when deposited from electrolyte with ultrasound agitation. Although the type of the substrate has the major influence on the adhesion strength, it can be said that Cu electrodeposition with ultrasonic mixing contributes to an increase in adhesion.",
publisher = "Srpsko hemijsko društvo, Beograd",
journal = "Journal of the Serbian Chemical Society",
title = "Mechanical characterization of copper coatings electrodeposited onto different substrates with and without ultrasound assistance",
pages = "741-729",
number = "7",
volume = "84",
doi = "10.2298/JSC181003023M"
}

Mladenović, I., Lamovec, J., Jović, V., Obradov, M., Vasiljević-Radović, D., Nikolić, N.,& Radojević, V.. (2019). Mechanical characterization of copper coatings electrodeposited onto different substrates with and without ultrasound assistance. in Journal of the Serbian Chemical Society
Srpsko hemijsko društvo, Beograd., 84(7), 729-741.
https://doi.org/10.2298/JSC181003023M

Mladenović I, Lamovec J, Jović V, Obradov M, Vasiljević-Radović D, Nikolić N, Radojević V. Mechanical characterization of copper coatings electrodeposited onto different substrates with and without ultrasound assistance. in Journal of the Serbian Chemical Society. 2019;84(7):729-741.
doi:10.2298/JSC181003023M .

Mladenović, Ivana, Lamovec, Jelena, Jović, Vesna, Obradov, Marko, Vasiljević-Radović, Dana, Nikolić, Nebojša, Radojević, Vesna, "Mechanical characterization of copper coatings electrodeposited onto different substrates with and without ultrasound assistance" in Journal of the Serbian Chemical Society, 84, no. 7 (2019):729-741,
https://doi.org/10.2298/JSC181003023M . .