Data-Driven Prediction Model of Components Shift during Reflow Process in Surface Mount Technology

Abstract

In surface mount technology (SMT), mounted components on soldered pads are subject to move during reflow process. This capability is known as self-alignment and is the result of fluid dynamic behavior of molten solder paste. This capability is critical in SMT because inaccurate self-alignment causes defects such as overhanging, tombstoning, etc. while on the other side, it can enable components to be perfectly self-assembled on or near the desire position. The aim of this study is to develop a machine learning model that predicts the components movement during reflow in x and y-directions as well as rotation. Our study is composed of two steps: (1) experimental data are studied to reveal the relationships between self-alignment and various factors including component geometry, pad geometry, etc. (2) advanced machine learning prediction models are applied to predict the distance and the direction of components shift using support vector regression (SVR), neural network (NN), and random forest regression (RFR). As a result, RFR can predict components shift with the average fitness of %99, %99, and %96 and with average prediction error of 13.47 (µm), 12.02 (µm), and 1.52 (deg.) for component shift in x, y, and rotational directions respectively. This enhancement provides the future capability of the parameters’ optimization in the pick and placement machine to control the best placement location and minimize the intrinsic defects caused by the self-alignment.