| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Phase Stability, Phase Transformations, and Reactive Phase Formation in Electronic Materials XXIV
|
| Presentation Title |
Exploring solder wetting angle by using machine learning approach |
| Author(s) |
Yu-Chen Liu, Bing-Xi Lee |
| On-Site Speaker (Planned) |
Yu-Chen Liu |
| Abstract Scope |
The solder wettability is a key factor for assessing joint reliability, as it directly relates to the soldering quality and the long-term stability of electronic products. This study utilizes machine learning method and Gaussian kernel ridge regression model to develop a surrogate model based on experimental contact angle data, aiming to explore the solder wettability as a function of alloy composition, reflow temperature and substrate composition. The model extrapolation to unknown alloys which were not shown in the data set showed a semi-quantitative or qualitative agreement with experimental reports. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Electronic Materials, Computational Materials Science & Engineering |