Simple Search:

Application of artificial neural networks for the optimisation of wetting contact angle for lead free Bi-Ag soldering alloys


Citation

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

Abstract / Synopsis

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.


Download File

[img]
Preview
PDF
17 JST(S)-0286-2017-2ndProof.pdf

Download (446kB) | Preview

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Faculty of Science
Institute of Advanced Technology
Institute of Tropical Forestry and Forest Products
Publisher: Universiti Putra Malaysia Press
Keywords: Artificial neural networks; Bi-Ag alloy; Lead free soldering alloy; Wetting angle
Depositing User: Nabilah Mustapa
Date Deposited: 10 Jan 2018 16:16
Last Modified: 25 Jan 2018 16:49
URI: http://psasir.upm.edu.my/id/eprint/58327
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item