  Quality control is of vital importance during electronics production. As the
methods of producing electronic circuits improve, there is an increasing chance
of solder defects during assembling the printed circuit board (PCB). Many
technologies have been incorporated for inspecting failed soldering, such as
X-ray imaging, optical imaging, and thermal imaging. With some advanced
algorithms, the new technologies are expected to control the production quality
based on the digital images. However, current algorithms sometimes are not
accurate enough to meet the quality control. Specialists are needed to do a
follow-up checking. For automated X-ray inspection, joint of interest on the
X-ray image is located by region of interest (ROI) and inspected by some
algorithms. Some incorrect ROIs deteriorate the inspection algorithm. The high
dimension of X-ray images and the varying sizes of image dimensions also
challenge the inspection algorithms. On the other hand, recent advances on deep
learning shed light on image-based tasks and are competitive to human levels.
In this paper, deep learning is incorporated in X-ray imaging based quality
control during PCB quality inspection. Two artificial intelligence (AI) based
models are proposed and compared for joint defect detection. The noised ROI
problem and the varying sizes of imaging dimension problem are addressed. The
efficacy of the proposed methods are verified through experimenting on a
real-world 3D X-ray dataset. By incorporating the proposed methods, specialist
inspection workload is largely saved.
