The design of quaternary eutectic solder by machine learning

TL;DR

This paper demonstrates how machine learning can effectively design quaternary eutectic solder alloys, accurately predicting compositions and eutectic points, thus accelerating development in electronic manufacturing.

Contribution

It introduces a machine learning approach for designing quaternary eutectic solder alloys, combining computational predictions with experimental validation.

Findings

Machine learning accurately predicted alloy compositions.

Experimental results closely matched ML predictions.

The approach accelerates eutectic solder development.

Abstract

In this paper, we obtain a Sn-Bi-In-Pb quaternary near eutectic alloy composition from machine learning model. The eutectic points and the alloy composition were evaluated and continuously improved by experimental input. The actual composition is near the result given by machine learning. We conclude that the application of machine learning in solder design has shown the potential to overcome the challenge in searching for the next generation eutectic solders, which will have a broad impact on the industry.