Integration of data-driven approach with theory,computation and experiment.• Free energy density function introduced for thermomigration.• Heat of transport for Cu in Cu 6 Sn 5 phase determined using neural network analysis.• Cold side IMC grain growth modeled using multi-phase field method.
Currently, in the era of big data and 5G communication technology, electromigration has become a serious reliability issue for the miniaturized solder joints used in microelectronic devices. Since the effective charge number (Z*) is considered as the driving force for electromigration, the lack of accurate experimental values for Z* pose severe challenges for simulation-aided design of electronic materials. In this work, a data-driven framework is developed to predict the Z* values of Cu and Sn species at the anode based LIQUID, Cu6Sn5 intermetallic compound (IMC) and FCC phases for the binary Cu-Sn system undergoing electromigration at 523.15 K. The growth rate constants (kem) of the anode IMC at several magnitudes of applied low current density (j=1× 10 6 to 10 × 10 6 A/m 2 ) are extracted from simulations based on a 1D multi-phase field model. A neural network employing Z* and j as input features, whereas utilizing these computed kem data as the expected output is trained. The results of the neural network analysis are optimized with experimental growth rate constants to estimate the effective charge numbers. For negligible increase in temperature at low j values, effective charge numbers of all phases are found to increase with current density and the increase is much more pronounced for the IMC phase. The predicted values of effective charge numbers Z* are then utilized in a 2D simulation to observe the anode IMC grain growth and electrical resistance changes in the multi-phase system. As the work consists the aspects of experiments, theory, computation and machine learning, it can be called the four paradigms approach for study of electromigration in Pb-free solder. Such a combination of multiple paradigms of materials design can be problem-solving for any future research scenario that is marked by uncertainties regarding determination of material properties.
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 2 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.
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