Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys
In the recent years, electronic packaging provides significant research and development challenges across multiple disciplines such as performance, materials, reliability, thermals and interconnections. New technologies and techniques frequently adopted can be implemented in soldering alloys of semi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universiti Putra Malaysia Press
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/58327/ http://psasir.upm.edu.my/id/eprint/58327/1/17%20JST%28S%29-0286-2017-2ndProof.pdf |
| _version_ | 1848853615688548352 |
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| author | Ghamarian, Nima Mohamed Ariff, Azmah Hanim Nahavandi, Mahdi Zainal, Zulkarnain Lim, Hong Ngee |
| author_facet | Ghamarian, Nima Mohamed Ariff, Azmah Hanim Nahavandi, Mahdi Zainal, Zulkarnain Lim, Hong Ngee |
| author_sort | Ghamarian, Nima |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In the recent years, electronic packaging provides significant research and development challenges across multiple disciplines such as performance, materials, reliability, thermals and interconnections. New technologies and techniques frequently adopted can be implemented in soldering alloys of semiconductor sectors in terms of optimisation. Wetting contact angle or wettability of solder alloys is one of the important factors which has got the attention of scholars. Hence in this study, due to the remarkable similarity over classical solder alloys (Pb-Sn), Bi-Ag solder was investigated. Data were collected through the effects of aging time variation and different weight percentages of Ag in solder alloys. The contact angle of the alloys with Cu plate was measured by optical microscopy. Artificial neural networks (ANNs) were applied on the measured datasets to develop a numerical model for further simulation. Results of the experiments and simulations showed that the coefficient of determination (R2) is around 0.97, which signifies that the ANN set up is appropriate for the evaluation. |
| first_indexed | 2025-11-15T10:56:48Z |
| format | Article |
| id | upm-58327 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:56:48Z |
| publishDate | 2017 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-583272018-01-25T08:49:52Z http://psasir.upm.edu.my/id/eprint/58327/ Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys Ghamarian, Nima Mohamed Ariff, Azmah Hanim Nahavandi, Mahdi Zainal, Zulkarnain Lim, Hong Ngee In the recent years, electronic packaging provides significant research and development challenges across multiple disciplines such as performance, materials, reliability, thermals and interconnections. New technologies and techniques frequently adopted can be implemented in soldering alloys of semiconductor sectors in terms of optimisation. Wetting contact angle or wettability of solder alloys is one of the important factors which has got the attention of scholars. Hence in this study, due to the remarkable similarity over classical solder alloys (Pb-Sn), Bi-Ag solder was investigated. Data were collected through the effects of aging time variation and different weight percentages of Ag in solder alloys. The contact angle of the alloys with Cu plate was measured by optical microscopy. Artificial neural networks (ANNs) were applied on the measured datasets to develop a numerical model for further simulation. Results of the experiments and simulations showed that the coefficient of determination (R2) is around 0.97, which signifies that the ANN set up is appropriate for the evaluation. Universiti Putra Malaysia Press 2017 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/58327/1/17%20JST%28S%29-0286-2017-2ndProof.pdf Ghamarian, Nima and Mohamed Ariff, Azmah Hanim and Nahavandi, Mahdi and Zainal, Zulkarnain and Lim, Hong Ngee (2017) Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys. Pertanika Journal of Science & Technology, 25 (4). pp. 1255-1260. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2025%20(4)%20Oct.%202017/17%20JST(S)-0286-2017-2ndProof.pdf |
| spellingShingle | Ghamarian, Nima Mohamed Ariff, Azmah Hanim Nahavandi, Mahdi Zainal, Zulkarnain Lim, Hong Ngee Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys |
| title | Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys |
| title_full | Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys |
| title_fullStr | Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys |
| title_full_unstemmed | Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys |
| title_short | Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys |
| title_sort | application of artificial neural networks for the optimisation of wetting contact angle for lead free bi-ag soldering alloys |
| url | http://psasir.upm.edu.my/id/eprint/58327/ http://psasir.upm.edu.my/id/eprint/58327/ http://psasir.upm.edu.my/id/eprint/58327/1/17%20JST%28S%29-0286-2017-2ndProof.pdf |