  In electronics manufacturing, solder joint defects are a common problem
affecting a variety of printed circuit board components. To identify and
correct solder joint defects, the solder joints on a circuit board are
typically inspected manually by trained human inspectors, which is a very
time-consuming and error-prone process. To improve both inspection efficiency
and accuracy, in this work we describe an explainable deep learning-based
visual quality inspection system tailored for visual inspection of solder
joints in electronics manufacturing environments. At the core of this system is
an explainable solder joint defect identification system called SolderNet which
we design and implement with trust and transparency in mind. While several
challenges remain before the full system can be developed and deployed, this
study presents important progress towards trustworthy visual inspection of
solder joints in electronics manufacturing.
