  The quality inspection of solder balls by detecting and measuring the void is
important to improve the board yield issues in electronic circuits. In general,
the inspection is carried out manually, based on 2D or 3D X-ray images. For
high quality inspection, it is difficult to detect and measure voids accurately
with high repeatability through the manual inspection and the process is time
consuming. In need of high quality and fast inspection, various approaches were
proposed, but, due to the various challenges like vias, reflections from the
plating or vias, inconsistent lighting, noise and void-like artifacts makes
these approaches difficult to work in all these challenging conditions. In
recent times, deep learning approaches are providing the outstanding accuracy
in various computer vision tasks. Considering the need of high quality and fast
inspection, in this paper, we applied U-Net to segment the void regions in
soldering balls. As it is difficult to get the annotated dataset covering all
the variations of void, we proposed an approach to generated the synthetic
dataset. The proposed approach is able to segment the voids and can be easily
scaled to various electronic products.
