Adaptive Hybrid Framework for Multiscale Void Inspection of Chip Resistor Solder Joints

Xiao, Pan; Xiao, Meng; Cai, Nian; Qiu, Baojun; Zhou, Shuai; Wang, Han

doi:10.1109/tim.2023.3235435

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…The malformed solder will form a cavity if it does not escape, generating voids, which will affect the thermal and electrical conductivity of the solder joint, lowering the device's reliability. [2] This article presents a new inspection approach to address the issue of using X-ray to inspect printed circuit boards (PCB) with imperfections. The concept uses automation to create interpretation improvements for better outcomes.…”

Section: Introductionmentioning

confidence: 99%

Advancing surface mount technology quality: a computer-assisted approach for enhanced X-ray inspection of solder joints

Terra,

Santiago,

Vieira

et al. 2024

Int J Adv Manuf Technol

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The electronics industry is one of the fastest evolution, innovative, and competitive industries. Quality standards have been improved to meet the high demands of modern products, and new technologies have been applied for defect detection to meet customers' requirements. The importance of image processing tools becomes inevitable. This work deals with images obtained through X-ray for inspection in PCBA to identify non-conformities and provides assertive interpretation in less time. Software associated with the database of images collected from the product will be applied, and the criteria from IPC standards will be used to verify compliance.

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Section: Introductionmentioning

confidence: 99%

Advancing surface mount technology quality: a computer-assisted approach for enhanced X-ray inspection of solder joints

Terra,

Santiago,

Vieira

et al. 2024

Int J Adv Manuf Technol

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Segmentation of void defects in X-ray images of chip solder joints based on PCB-DeepLabV3 algorithm

Kong,

Hu,

Gong

et al. 2024

Sci Rep

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Defects within chip solder joints are usually inspected visually for defects using X-ray imaging to obtain images. The phenomenon of voids inside solder joints is one of the most likely types of defects in the soldering process, and accurate detection of voids becomes difficult due to their irregular shapes, varying sizes, and defocused edges. To address this problem, an X-ray void image segmentation algorithm based on improved PCB-DeepLabV3 is proposed. Firstly, to meet the demand for lightweight and easy deployment in industrial scenarios, mobilenetv2 is used as the feature extraction backbone network of the PCB-DeepLabV3 model; then, Attentional multi-scale two-space pyramid pooling network (AMTPNet) is designed to optimize the shallow feature edges and to improve the ability to capture detailed information; finally, image cropping and cleaning methods are designed to enhance the training dataset, and the improved PCB-DeepLabV3 is applied to the training dataset. The improved PCB-DeepLabV3 model is used to segment the void regions within the solder joints and compared with the classical semantic segmentation models such as Unet, SegNet, PSPNet, and DeeplabV3. The proposed new method enables the solder joint void inspection to get rid of the traditional way of visual inspection, realize intelligent upgrading, and effectively improve the problem of difficult segmentation of the target virtual edges, to obtain the inspection results with higher accuracy.

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X-ray Void Defect Detection in Chip Solder Joints Based on PCB-DeepLabV3 Algorithm

Kong,

Hu,

Gong

et al. 2023

Preprint

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Defects within chip solder joints are usually inspected visually for defects using X-ray imaging to obtain images. The phenomenon of voids inside solder joints is one of the most likely types of defects in the soldering process, and accurate detection of voids becomes difficult due to their irregular shapes, varying sizes, and defocused edges. To address this problem, an X-ray void image segmentation algorithm based on improved PCB-DeepLabV3 is proposed. Firstly, to meet the demand for lightweight and easy deployment in industrial scenarios, mobilenetv2 is used as the feature extraction backbone network of the PCB-DeepLabV3 model; then, Attention Multiscale Spatial Double Pyramid Network (AMTPNet) is designed to optimize the shallow feature edges and to improve the ability to capture detailed information; finally, image cropping and cleaning methods are designed to enhance the training dataset, and the improved PCB-DeepLabV3 is applied to the training dataset. The improved PCB-DeepLabV3 model is used to segment the void regions within the solder joints and compared with the classical semantic segmentation models such as Unet, SegNet, PSPNet, and DeeplabV3. The proposed new method enables the solder joint void inspection to get rid of the traditional way of visual inspection, realize intelligent upgrading, and effectively improve the problem of difficult segmentation of the target virtual edges, to obtain the inspection results with higher accuracy.

show abstract

Adaptive Hybrid Framework for Multiscale Void Inspection of Chip Resistor Solder Joints

Cited by 4 publications

References 31 publications

Advancing surface mount technology quality: a computer-assisted approach for enhanced X-ray inspection of solder joints

Advancing surface mount technology quality: a computer-assisted approach for enhanced X-ray inspection of solder joints

Segmentation of void defects in X-ray images of chip solder joints based on PCB-DeepLabV3 algorithm

X-ray Void Defect Detection in Chip Solder Joints Based on PCB-DeepLabV3 Algorithm

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