Prediction of Component Shifts in Pick and Place Process of Surface Mount Technology Using Support Vector Regression

Abstract In pick and place (P&P) process of surface mount technology (SMT) the placed component can shift from its ideal (or designed) position on the wet solder paste. The solder paste with some fluid properties could slump and the unbalance between different sides of solder paste can lead to other forces on the components as well. Though the shifts are usually considered to be negligible and can be made up to some extent by the following self-alignment during the process of soldering reflow, it should be attracted attention as its importance for addressing the quality of the printed circuit board (PCB) in SMT. To minimize or control the component shifts, whose relationship with the characteristics of the solder paste (e.g., offset, volume) should be studied initially. In this paper, we design a comprehensive experiment and collect the data from a state-of-the-art SMT assembly line. Then we use support vector regression (SVR) model to predict the component shifts based on different situations of solder paste and placement settings. Also, two kernel functions, linear (SVR-Linear) and radial basis function (SVR-RBF), are employed. The achieved results indicate that the component shift in P&P process is significant, and the SVR model is highly qualified for the forecast of the component shifts. Particularly, the SVR-RBF model outperforms the SVR-Linear model considering the prediction error.