The effect of solder paste volume on surface mount assembly self- alignment

In surface mount assembly, the consistency of solder paste volumes and the placement of the components play a vital role in the movement of the chip components when they go through the reflow soldering process. These movements may lead to common assembly defects such as overhanging, tilting, nonwetting, and tombstoning. Components to be assembled in surface mount assembly have self-alignment nature, which is driven by the total internal energy including gravitational energy, surface tension energy, etc. in the molten solder attributed to various factors. The factors include 1) solder paste composition, surface energy, gravity; 2) process variations such as solder paste volume, solder paste location, the inequality of solder paste volumes at the two terminals of the passive component, and components location and orientation; 3) design parameters such as pad dimension, pad-to-pad spacing, and pad surface finish. In this research, energy-based three-dimensional model is created to predict solder joint profile (shape). Three types of passive chip component are chosen to be the modelling examples to investigate the component self-alignment performance during the reflow soldering process. By nature, the molten solder being a bulk fluid always tends to minimize its internal energy towards mechanical equilibrium and it causes the passive component to move to the state of minimal energy. This movement leads the component to self-align. Thus, by creating an energy minimization model, we can simulate the process of component self-alignment and predict the solder joint profile, final location, and orientation of the passive component. Experimental studies are performed to develop a data-driven prediction model and are compared with the simulated component for validation. The passive components are placed with intended misplacements and their positions are measured before and after soldering. A wide range of surface mount assembly imperfections are considered and intentionally studied to achieve a powerful dataset and better convergence. (C) 2019 The Authors. Published by Elsevier B.V.