About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Phase Stability, Phase Transformations, and Reactive Phase Formation in Electronic Materials XXVI
|
| Presentation Title |
How to design high-strength Sn-In solders using CALPHAD and machine learning approach |
| Author(s) |
Yu-Chen Liu, Hao-Wei Kuo, Yu-Chia Chang |
| On-Site Speaker (Planned) |
Yu-Chen Liu |
| Abstract Scope |
The growing demand for flexible electronics requires reliable low-temperature interconnects, making eutectic Sn-In alloys (melting point 118 °C) attractive candidates. However, their inherently low mechanical strength limits broad applicability. To overcome this critical barrier, we demonstrate how to design high-strength Sn-In-X (X = Cu, Ag, Zn, Bi, Sb, Al, Ni) alloys by seamlessly integrating the CALPHAD thermodynamic approach with machine learning model. The data-driven model successfully captures complex composition-strength relationships, achieving a robust testing R² of 0.91. Utilizing this integrated framework, we efficiently screened and discovered a Sn–47In–1.59Ag–0.27Cu alloy. Experimental validation confirms that the designed alloy achieves an ultimate tensile strength of 26.6 MPa—approximately three times that of eutectic Sn-52In—while simultaneously maintaining a reasonable elongation of 21.9%. This integrated methodology provides a highly efficient pathway to accelerate the discovery of advanced low-temperature solders. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Electronic Materials, Machine Learning, Modeling and Simulation |