Semi-Supervised Automated Layer Identification of X-ray Tomography Imaged PCBs

Botero, Ulbert J.; Ganji, Fatemeh; Asadizanjani, Navid; Forte, Domenic

doi:10.1109/paine49178.2020.9337738

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…In order to evaluate the quality of sample segmentation results and introduce task-related information into the data selection process, we proposed RQEM. As shown in Figure 2b, the module consists of convolutional layers and fully connected layers, which takes the segmentation result of the sample as input, and then outputs the quality information In fo RQE (x) as shown in Equation (7).…”

Section: Proposed Results Quality Evaluation Modulementioning

confidence: 99%

Section: Non-destructive Testingmentioning

confidence: 99%

“…Reference [4] proposed a method for the task, due to the need to use some external equipment such as cameras to image the printed circuit board, which leads to the destruction of the original device. In order to achieve a better non‐destructive testing, [6, 7] used computed tomography to scan the printed circuit board in electronic equipment, and then used some image processing methods to obtain better quality image scanning data. As a key step in the whole process, the segmentation of elements in PCB CT images provides the basis for the subsequent further detection, and is related to the efficiency and accuracy of the whole process.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, in order to achieve a better detection effect, the original electronic equipment will inevitably be damaged, resulting in the inability to reuse. By imaging printed circuit boards in electronic equipment using computed tomography technology, the internal structure of all layers can be shown, and some nondestructive testing methods [5][6][7] have been developed based on it. In addition to that, some of the more advanced approaches [8][9][10][11] are beginning to explore the use of deep learning models to detect and segment elements of the PCB computed tomography (CT) images, such as holes and wires etc.…”

Section: Introductionmentioning

confidence: 99%

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An active learning method based on result quality evaluation for printed circuit board computed tomography image segmentation

Song,

Qiao,

Yang

et al. 2023

IET Image Processing

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Element detection is a key step in non‐destructive testing of printed circuit board (PCB) based on computed tomography (CT). In recent years, some image segmentation methods based on deep learning have shown great potential in the element segmentation task of PCB CT images, and greatly improved the efficiency and accuracy. However, since image segmentation is based on pixel‐level classification, the annotation of training data is difficult and costly. Aiming at this problem, the authors proposed a new active learning method based on the integration of relevant information about segmentation tasks and data variance. In this method, the Result Quality Evaluation Module (RQEM) proposed by us is used to generate task‐related information, and an adversarial network is used to generate the difference information between samples and the initial labelled data. Finally, the two parts of information are fused and used as the standard of data selecting. In the PCB CT image element segmentation task, the authors only need to select 12.7% of the whole training set with their proposed method to make Mean Intersection Over Union (MIOU) reach 79.7, which has reached 95% of the optimal performance of 83.7. The in‐depth analysis also verifies the effectiveness and stability of the authors’ method.

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Section: Proposed Results Quality Evaluation Modulementioning

confidence: 99%

Section: Non-destructive Testingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An active learning method based on result quality evaluation for printed circuit board computed tomography image segmentation

Song,

Qiao,

Yang

et al. 2023

IET Image Processing

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“…However, it is only suitable for those with a ball grid array. The PCB reverse engineering through X-ray computed tomography (CT) is also proposed in [7]. Nevertheless, the X-ray CT framework needs a complicated configuration process to estimate exactly the separate sides of the PCB structure.…”

Section: Introductionmentioning

confidence: 99%

A real-time defect detection in printed circuit boards applying deep learning

Nguyen

Bui

2022

Eureka: PE

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Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustness

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Automated layer identification and segmentation of x‐ray computer tomography imaged PCBs

et al. 2023

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The non‐destructive inspection of Printed Circuit Boards (PCBs) through r‐ray Computer Tomography (CT) is a recently developed method that offers several advantages over traditional inspection techniques. This method is non‐invasive, quick, and offers high resolution, leading to significant improvements in inspection and repair efficiency. Post‐image analysis is an important step in PCB inspection and has important practical significance for automatic positioning and determining the location of faults. Usually, the results of image segmentation are an important basis for PCB defect detection, and accurate segmentation results can effectively improve the efficiency and accuracy of PCB inspection and increase the level of automation. This paper discusses two innovative improvements for the automatic segmentation process: firstly determining which slices of an x‐ray CT 3D PCB stack belong to which layer on a physical PCB in an automatic, generic and completely unsupervised way, which is verified on a 4‐layer PCB; secondly proposing a level set‐based image segmentation algorithm for the problem of gray scale inhomogeneity present in PCB CT. Experimental results on real PCB CT images with high aliasing and artifacts show that the proposed model can obtain better performance than the popular active contour models.

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Semi-Supervised Automated Layer Identification of X-ray Tomography Imaged PCBs

Cited by 7 publications

References 7 publications

An active learning method based on result quality evaluation for printed circuit board computed tomography image segmentation

An active learning method based on result quality evaluation for printed circuit board computed tomography image segmentation

A real-time defect detection in printed circuit boards applying deep learning

Automated layer identification and segmentation of x‐ray computer tomography imaged PCBs

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