Solder Joint Inspection on Printed Circuit Boards: A Survey and a Dataset


IEEE Transactions on Instrumentation and Measurement, vol.72, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 72
  • Publication Date: 2023
  • Doi Number: 10.1109/tim.2023.3277935
  • Journal Name: IEEE Transactions on Instrumentation and Measurement
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Automated optical inspection (AOI), electronics manufacturing, printed circuit board (PCB) inspection, solder joint inspection
  • Hacettepe University Affiliated: Yes


Surface Mount Technology (SMT) is a procedure for mounting electronic components to the surface of the printed circuit boards (PCB). Although the SMT procedure is more reliable than the conventional through-hole mounting, many errors may occur in SMT lines. In this paper, 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 3D reconstruction. We compare and discuss these approaches according to their advantages and disadvantages, with a focus on the more recent deep learning and 3D reconstruction techniques, and list the public datasets for solder joints 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