Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters
2019
Authors
Mladenović, IvanaLamovec, Jelena
Jović, Vesna
Obradov, M.
Radulović, Katarina
Vasiljević-Radović, Dana
Radojević, Vesna
Conference object (Published version)
Metadata
Show full item recordAbstract
Copper coatings are produced on silicon wafer by electrodeposition (ED) for various cathode current densities. The resulting composite systems consist of 10 μm monolayered copper films electrodeposited from sulphate bath on Si wafers with sputtered layers of Cr/Au. Hardness measurements were performed to evaluate properties of the composites. The composite hardness (H c ) was characterized using Vickers microindentation test. Then, an artificial neural network (ANN) model was used to study the relationship between the parameters of metallic composite and their hardness. Two experimental values: applied load during indentation test and current density during the ED process were used as the inputs to the neural network. Finally, the results of the composite hardness (experimental and predicted) were used to estimate the film hardness (H f ) of copper for each variations of the current density. This article shows that ANN is an useful tool in modeling composite hardness change with variat...ion of experimental parameters predicting hardness change of composite Si/Cu with average error of 6 %. Using created ANN model it is possible to predict microhardness of Cu film for current density or indentation load for which we do not have experimental data.
Keywords:
Films / Artificial neural networks / Predictive models / Current density / Substrates / Load modeling / CopperSource:
2019 IEEE 31st International Conference on Microelectronics, MIEL 2019 - Proceedings, 2019, 133-136Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
Funding / projects:
- Micro- Nanosystems and Sensors for Electric Power and Process Industry and Environmental Protection (RS-MESTD-Technological Development (TD or TR)-32008)
- Predefined functional properties polymer composite materials processes and equipment development (RS-MESTD-Technological Development (TD or TR)-34011)
Institution/Community
Tehnološko-metalurški fakultetTY - CONF AU - Mladenović, Ivana AU - Lamovec, Jelena AU - Jović, Vesna AU - Obradov, M. AU - Radulović, Katarina AU - Vasiljević-Radović, Dana AU - Radojević, Vesna PY - 2019 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4186 AB - Copper coatings are produced on silicon wafer by electrodeposition (ED) for various cathode current densities. The resulting composite systems consist of 10 μm monolayered copper films electrodeposited from sulphate bath on Si wafers with sputtered layers of Cr/Au. Hardness measurements were performed to evaluate properties of the composites. The composite hardness (H c ) was characterized using Vickers microindentation test. Then, an artificial neural network (ANN) model was used to study the relationship between the parameters of metallic composite and their hardness. Two experimental values: applied load during indentation test and current density during the ED process were used as the inputs to the neural network. Finally, the results of the composite hardness (experimental and predicted) were used to estimate the film hardness (H f ) of copper for each variations of the current density. This article shows that ANN is an useful tool in modeling composite hardness change with variation of experimental parameters predicting hardness change of composite Si/Cu with average error of 6 %. Using created ANN model it is possible to predict microhardness of Cu film for current density or indentation load for which we do not have experimental data. PB - Institute of Electrical and Electronics Engineers (IEEE) C3 - 2019 IEEE 31st International Conference on Microelectronics, MIEL 2019 - Proceedings T1 - Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters EP - 136 SP - 133 DO - 10.1109/MIEL.2019.8889610 ER -
@conference{ author = "Mladenović, Ivana and Lamovec, Jelena and Jović, Vesna and Obradov, M. and Radulović, Katarina and Vasiljević-Radović, Dana and Radojević, Vesna", year = "2019", abstract = "Copper coatings are produced on silicon wafer by electrodeposition (ED) for various cathode current densities. The resulting composite systems consist of 10 μm monolayered copper films electrodeposited from sulphate bath on Si wafers with sputtered layers of Cr/Au. Hardness measurements were performed to evaluate properties of the composites. The composite hardness (H c ) was characterized using Vickers microindentation test. Then, an artificial neural network (ANN) model was used to study the relationship between the parameters of metallic composite and their hardness. Two experimental values: applied load during indentation test and current density during the ED process were used as the inputs to the neural network. Finally, the results of the composite hardness (experimental and predicted) were used to estimate the film hardness (H f ) of copper for each variations of the current density. This article shows that ANN is an useful tool in modeling composite hardness change with variation of experimental parameters predicting hardness change of composite Si/Cu with average error of 6 %. Using created ANN model it is possible to predict microhardness of Cu film for current density or indentation load for which we do not have experimental data.", publisher = "Institute of Electrical and Electronics Engineers (IEEE)", journal = "2019 IEEE 31st International Conference on Microelectronics, MIEL 2019 - Proceedings", title = "Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters", pages = "136-133", doi = "10.1109/MIEL.2019.8889610" }
Mladenović, I., Lamovec, J., Jović, V., Obradov, M., Radulović, K., Vasiljević-Radović, D.,& Radojević, V.. (2019). Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters. in 2019 IEEE 31st International Conference on Microelectronics, MIEL 2019 - Proceedings Institute of Electrical and Electronics Engineers (IEEE)., 133-136. https://doi.org/10.1109/MIEL.2019.8889610
Mladenović I, Lamovec J, Jović V, Obradov M, Radulović K, Vasiljević-Radović D, Radojević V. Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters. in 2019 IEEE 31st International Conference on Microelectronics, MIEL 2019 - Proceedings. 2019;:133-136. doi:10.1109/MIEL.2019.8889610 .
Mladenović, Ivana, Lamovec, Jelena, Jović, Vesna, Obradov, M., Radulović, Katarina, Vasiljević-Radović, Dana, Radojević, Vesna, "Artificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parameters" in 2019 IEEE 31st International Conference on Microelectronics, MIEL 2019 - Proceedings (2019):133-136, https://doi.org/10.1109/MIEL.2019.8889610 . .