Enhanced X-Ray Inspection of Solder Joints in SMT Electronics Production using Convolutional Neural Networks

Schmidt, Konstantin; Thielen, Nils; Voigt, Christian; Seidel, Reinhardt; Franke, Jörg; Milde, Yannik; Bönig, Jochen; Beitinger, Gunter

doi:10.1109/siitme50350.2020.9292292

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…However, the improved YOLO-v3 was not efficient in identifying some components such as resistors and capacitors. Schmidt et al [12] proposed an X-ray imaging system for solder joint inspection in surface mount technology (SMT) electronics production using a convolutional neural network (CNN). They used 2D grayscale images to feed the CNN classifier.…”

Section: Related Workmentioning

confidence: 99%

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Starodubov,

Danishvar,

Abu Ebayyeh

et al. 2024

Electronics

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Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.

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Section: Related Workmentioning

confidence: 99%

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Starodubov,

Danishvar,

Abu Ebayyeh

et al. 2024

Electronics

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“…These defects affect the solder balls' conductivity and consequently lead to intermittent failures. In another study [97] focusing on solder joints, solder voids and head-in-pillow defects were recognized.…”

Section: G Othersmentioning

confidence: 99%

“…VGG-16 architecture shown in Fig. 7-b was used in [97] for solder joint classification and in [40] along with spatial attention and bilinear pooling for casting defects classification. VGG-16 was used as a feature map extractor (backbone network) in an anomaly detector network for casting defect localization in [44] and in an object detection network for X-ray baggage security assessment in [64].…”

Section: ) Deep Classification and Backbone Architecturesmentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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“…However, defects such as leakage welding cannot be detected by the X-ray detection method due to the weak absorption of X-rays by bubbles and cracks. Infrared detection detects solder joint defects by applying thermal excitation near the joint and then extracting and analyzing the infrared image (Schmidt et al ., 2020). Although most of the solder joint defects can be recognized by the infrared detection method, it requires strict ambient temperature, cannot be detected in real time and the measurement equipment is costly.…”

Section: Introductionmentioning

confidence: 99%

Robust vision detection of pipeline solder joints

An,

Kong

2024

SSMT

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Purpose Solder joint inspection plays a critical role in various industries, with a focus on integrated chip (IC) solder joints and metal surface welds. However, the detection of tubular solder joints has received relatively less attention. This paper aims to address the challenges of detecting small targets and complex environments by proposing a robust visual detection method for pipeline solder joints. The method is characterized by its simplicity, cost-effectiveness and ease of implementation. Design/methodology/approach A robust visual detection method based on the characteristics of pipeline solder joints is proposed. With the improved hue, saturation and value (HSV) color space, the method uses a multi-level template matching approach to first segment the pipeline from the background, and then match the endpoint of the pipeline to accurately locate the solder joint. The proposed method leverages the distinctive characteristics of pipeline solder joints and employs an enhanced HSV color space. A multi-level template matching approach is utilized to segment the pipeline from the background and accurately locate the solder joint by matching the pipeline endpoint. Findings The experimental results demonstrate the effectiveness of the proposed solder joint detection method in practical detection tasks. The average precision of pipeline weld joint localization exceeds 95%, while the average recall is greater than 90%. These findings highlight the applicability of the method to pipeline solder joint detection tasks, specifically in the context of production lines for refrigeration equipment. Research limitations/implications The precision of the method is influenced by the placement angle and lighting conditions of the test specimen, which may pose challenges and impact the algorithm's performance. Potential avenues for improvement include exploring deep learning methods, incorporating additional features and contextual information for localization, and utilizing advanced image enhancement techniques to improve image quality. Originality/value The proposed pipeline solder joint detection method offers a novel and practical approach. The simplicity, cost-effectiveness and ease of implementation make it an attractive choice for detecting pipeline solder joints in different industrial applications.

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Enhanced X-Ray Inspection of Solder Joints in SMT Electronics Production using Convolutional Neural Networks

Cited by 13 publications

References 8 publications

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Robust vision detection of pipeline solder joints

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