A data-driven framework to predict the morphology of interfacial Cu6Sn5 IMC in SAC/Cu system during laser soldering

A data-driven approach combining together the experimental laser soldering, finite element analysis and machine learning, has been utilized to predict the morphology of interfacial intermetallic compound (IMC) in Sn-xAg-yCu/Cu (SAC/Cu) system. Six types of SAC solders with varying weight proportion of Ag and Cu, have been processed with fiber laser at different magnitudes of power (30−50 W) and scan speed (10−240 mm/min), and the resultant IMC morphologies characterized through scanning electron microscope are categorized as prismatic and scalloped ones. For the different alloy composition and laser parameters, finite element method (FEM) is employed to compute the transient distribution of temperature at the interface of solder and substrates. The FEM-generated datasets are supplied to a neural network that predicts the IMC morphology through the quantified values of temperature dependent Jackson parameter (αJ). The numerical value of αJ predicted from neural network is validated with experimental IMC morphologies. The critical scan speed for the morphology transition between prismatic and scalloped IMC is estimated for each solder composition at a given power. Sn-0.7Cu having the largest critical scan speed at 30 W and Sn-3.5Ag alloy having the largest critical scan speed at input power values of 40 W and 50 W, thus possessing the greatest likelihood of forming prismatic interfacial IMC during laser soldering, can be inferred as most suitable SAC solders in applications exposed to shear loads.