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JBE, vol. 26, no. 4, pp.409-418, July, 2021

DOI: https://doi.org/10.5909/JBE.2021.26.4.409

A Problematic Bubble Detection Algorithm for Conformal Coated PCB Using Convolutional Neural Networks

Dong Hee Lee, SungRyung Cho, Kyeong-Hoon Jung, and Dong Wook Kang

C.A E-mail: dwkang@kookmin.ac.kr

Abstract:

Conformal coating is a technology that protects PCB(Printed Circuit Board) and minimizes PCB failures. Since the defects in the coating are linked to failure of the PCB, the coating surface is examined for air bubbles to satisfy the successful conditions of the conformal coating. In this paper, we propose an algorithm for detecting problematic bubbles in high-risk groups by applying image signal processing. The algorithm consists of finding candidates for problematic bubbles and verifying candidates. Bubbles do not appear in visible light images, but can be visually distinguished from UV(Ultra Violet) light sources. In particular the center of the problematic bubble is dark in brightness and the border is high in brightness. In the paper, these brightness characteristics are called valley and mountain features, and the areas where both characteristics appear at the same time are candidates for problematic bubbles. However, it is necessary to verify candidates because there may be candidates who are not bubbles. In the candidate verification phase, we used convolutional neural network models, and ResNet performed best compared to other models. The algorithms presented in this paper showed the performance of precision 0.805, recall 0.763, and f1-score 0.767, and these results show sufficient potential for bubble test automation.



Keyword: Problematic Bubble, Bubble Detection, Conformal Coating, CNN, ResNet

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