A Compact High-Resolution Resonance-Based Capacitive Sensor for Defects Detection on PCBAs

Qiu, Tie; Tek, Choon Kait Andrew; Huang, Shao Ying

doi:10.1109/access.2020.3036884

Cited by 6 publications

(2 citation statements)

References 27 publications

(41 reference statements)

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“…Qiu et al [29] are developed a capacitive resonator sensor for defect detection (shorts and opens on metallic traces) on PCBAs. The structure and functionality of the sensor is explained, showing its potential for usage in manufacturing.…”

Section: Investigation Concerning Pcba and Testingmentioning

confidence: 99%

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Petkov,

Ivanova

2024

Bulletin EEI

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Testing and visual inspection of printed circuit boards (PCBs) and printed circuit board assemblies (PCBAs) are important procedures in the manufacturing process of electronic modules and devices related to locating and identifying possible defects and failures. Earlier defects detection leads to decreasing expenses, time and used resources to produce high quality electronics. In this paper an exploration and analysis about the current research regarding methods for PCB and PCBA testing, techniques for defects detection and vusial inspection is performed. The impact of machine and deep learning for testing and visual inspection procedures is also investigated. The used methodology comprises bibliometric approach and content analysis of papers, indexed in scientific database Scopus, considering the queries: “PCB and testing” and “PCB and testing”, “printed circuit board assembly and testing” and “PCBA and testing”, “PCB defect detection” and “PCBA defect detection”, “PCB and visual inspection”, and “PCBA and visual inspection”. The findings are presented in the form of a framework, which summarizes the contemporary landscape of methods for PCBs and PCBAs testing and visual inspection.

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Section: Investigation Concerning Pcba and Testingmentioning

confidence: 99%

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Petkov,

Ivanova

2024

Bulletin EEI

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“…In consideration of various non-anti-interference effects, the collected PCB images need to be pre-treatment. After image registration, imabsdiff function differential operated to locate defects [9]. Finally feature extraction and defect classification based on MobileNet-V2 improved fully convolutional neural networks model, so as to realize the whole PCB bare board defect identification [10] - [16] algorithm flow.…”

Section: Algorithm Flowmentioning

confidence: 99%

Printed Circuit Boards Defect Detection Method Based on Improved Fully Convolutional Networks

et al. 2022

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Since printed circuit board (PCB) is the key to ensure the reliability of electronic equipment. Therefore, defect detection for PCB is a basic and necessary work. This paper proposes a PCB defect detection method based on an improved fully convolutional neural networks to detect four types of defects: spurs, mouse bites, short circuits and open circuits. The improved neural networks increase the receptive field and solve the problems of ordinary atrous convolution information loss of continuity by introducing the continuous atrous convolution module. At the same time, we introduce the improved skip layer after the upsampling layer to get the characteristics of multi-scale fusion, improve the resolution of the image. Due to the fewer levels of the whole networks, MobileNet-V2 networks model is adopted in the improved fully convolutional neural networks, which is compared with ResNet-50 and Vgg-16 networks model. Comparison of accuracy of four kinds of PCB classification defects. Experimental results show that the improved fully convolutional neural networks based on MobileNet-V2 have an average recognition accuracy of 92.86% for four types of PCB defects, which is better than the ResNet-50 and Vgg-16 networks models, verifying the effectiveness of the PCB defect recognition classification. Moreover, experiments are carried out in a real environment to verify the feasibility of the proposed method.INDEX TERMS Fully convolutional neural networks, PCB defect detection, continuous atrous convolution, skip structure.

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Telemetric QCM-D based sensing system with adaptive excitation frequency

Addabbo

Carli

Fort

et al. 2022

2022 IEEE Sensors Applications Symposium (SAS)

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A Compact High-Resolution Resonance-Based Capacitive Sensor for Defects Detection on PCBAs

Cited by 6 publications

References 27 publications

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Printed Circuit Boards Defect Detection Method Based on Improved Fully Convolutional Networks

Telemetric QCM-D based sensing system with adaptive excitation frequency

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