  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 behaviour 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 (um), 12.02 (um), 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.
