TY - GEN
T1 - Machine Learning Models for PCB Defect Detection
T2 - 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
AU - Subbulakshmi, T.
AU - Ramachandran, Vishwath
AU - Anand, Philip
AU - Madhavan, Rohit
AU - Revathi, M.
AU - Subramanian, Girish H.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Almost every commonly used electronic device relies on a Printed Circuit Board (PCB) produced through Surface-Mount Technology (SMT). Thanks to advancements that have significantly reduced the size of electronic components, PCBs have dramatically decreased in size. Consequently, it is crucial to conduct the application and examination of solder paste during SMT with a strong emphasis on speed and precision. This research survey delves into an analysis of Solder Paste Inspection (SPI) methods, drawing insights from more than 30 research papers spanning several decades. Initially, the survey identifies various PCB defect detection methods and assembles relevant papers for each approach. Subsequently, it provides a brief assessment of these papers, comparing their algorithms, performance, advantages, and challenges. This comprehensive overview aims to offer fresh perspectives to researchers and readers interested in gaining a deeper understanding of the latest developments in solder paste inspection and the detection of defects in PCBs.
AB - Almost every commonly used electronic device relies on a Printed Circuit Board (PCB) produced through Surface-Mount Technology (SMT). Thanks to advancements that have significantly reduced the size of electronic components, PCBs have dramatically decreased in size. Consequently, it is crucial to conduct the application and examination of solder paste during SMT with a strong emphasis on speed and precision. This research survey delves into an analysis of Solder Paste Inspection (SPI) methods, drawing insights from more than 30 research papers spanning several decades. Initially, the survey identifies various PCB defect detection methods and assembles relevant papers for each approach. Subsequently, it provides a brief assessment of these papers, comparing their algorithms, performance, advantages, and challenges. This comprehensive overview aims to offer fresh perspectives to researchers and readers interested in gaining a deeper understanding of the latest developments in solder paste inspection and the detection of defects in PCBs.
UR - http://www.scopus.com/inward/record.url?scp=85190677749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85190677749&partnerID=8YFLogxK
U2 - 10.1109/ICRTAC59277.2023.10480774
DO - 10.1109/ICRTAC59277.2023.10480774
M3 - Conference contribution
AN - SCOPUS:85190677749
T3 - Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
SP - 731
EP - 735
BT - Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023
A2 - Ganesan, R.
A2 - Harikrishnan, K.
A2 - Parvathi, R.
A2 - Geetha, S.
A2 - Thomas Abraham, J.V.
A2 - Vedhapriyavadhana, R.
A2 - Murugesan, Rajkumar
A2 - Kalaipriyan, T.
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2023 through 15 December 2023
ER -