Ural, Dogan IrmakSezen, ArdaComputer Engineering2024-07-052024-07-05202401864-59091864-591710.1007/s12065-024-00930-x2-s2.0-85189201281https://doi.org/10.1007/s12065-024-00930-xhttps://hdl.handle.net/20.500.14411/2244SMT (Surface Mount Technology) has been the backbone of PCB (Printed Circuit Board) production for the last couple of decades. Even though the speed and accuracy of SMT have been drastically improved in the last decade, errors during production are still a very valid problem for the PCB industry. With the exponential rise of Artificial Intelligence in the last decade, the SMT industry was one of the most eager industries to use this new technology to detect possible defects during production. Lately, traditional image processing techniques started to lag behind methods such as machine learning and deep learning when the discussion came to the need of high accuracy. In this paper, we screen academic libraries to understand which of the latest methods and techniques are used in the domain and to deduce a general process for detecting defects in PCBs. During the research we have investigated research questions related to state-of-the-art methods, highly mentioned datasets, and sought after PCB defects. All findings and answers are mapped to be able to understand where this pursuit might point towards. From a total of 270 papers, 90 of them were addressed in detail and 78 papers were chosen for this systematic mapping.eninfo:eu-repo/semantics/closedAccessArtificial intelligenceImage processingDefect detectionPCBSystematic mappingReviewQ2WOS:001195694000001