LAUSR

Surface mount technology (SMT) is a procedure for mounting electronic components to the surface of printed circuit boards (PCBs). Although the SMT procedure is more reliable than the conventional through-hole mounting, many errors may occur in SMT lines. In this article, we surveyed methods presented for the optical inspection of solder joints on PCBs. The methods are grouped by problem-solving approach: reference-based, machine learning/computer vision, deep learning, and 3-D reconstruction. We compare and discuss these approaches according to their advantages and disadvantages, with a focus on the more recent deep learning and 3-D reconstruction techniques, and list the public datasets for solder joint inspection. Since defective samples rarely occur in the SMT manufacturing lines, public datasets have few defective samples that are essential for the inspection task. To fill this gap, we publish a public dataset with both normal and different types of SMT errors. Our dataset can be used as a benchmark to compare different algorithms. The dataset is available at https://github.com/furkanulger/solder-joint-dataset.