Defect detection of flip-chip solder joints using modal analysis

Liu, Junchao; Shi, Tielin; Wang, Ke; Tang, Zirong; Liao, Guanglan

doi:10.1016/j.microrel.2012.06.135

Cited by 7 publications

(3 citation statements)

References 25 publications

(24 reference statements)

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“…e top module directly uses 1 × 1 convolution processing on the input features to obtain spatial global features [19]. e multilevel pooling module contains 3 downsampling parts to obtain three different resolution features of 1/2 input image, 1/4 input image, and 1/8 input image, respectively, and obtain context information of different scales until the input is restored feature size [20]. Finally, it is combined with the top module level as the final output of the structure [21].…”

Section: Recognition Methods Of Recursive Attention Model Formentioning

confidence: 99%

Casting Defect Detection and Classification of Convolutional Neural Network Based on Recursive Attention Model

Zhao

2022

Scientific Programming

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The method based on deep learning shows excellent performance in the recognition and classification of surface defects of some industrial products. The method based on deep learning has high efficiency in the identification and classification of surface defects of industrial products, and the false detection rate and missed detection rate are relatively low. However, the recognition accuracy of defect detection and classification of most industrial products needs to be improved, especially for those with similar contours and relatively large structural different casting. This paper takes casting defect detection as the goal and proposes a convolutional neural network casting defect detection and classification (RCNN-DC) algorithm based on the recursive attention model. Through this model, the casting can be better identified and detected, and casting defects can be avoided as much as possible, which is of great significance to the technological development of the industry. First, use a large amount of readily available defect-free sample data to detect anomalous defects. Next, we compare the accuracy and performance of the detection model and the general recognition model. The research results show that the test effect of the RCNN-DC casting defect detection network model is significantly better than the traditional detection model, with a classification accuracy of 96.67%. Then, we compare the RCNN-DC network with three classic popular networks, GooGleNet, ResNet-50, and AlexNet. Among them, AlexNet and ResNet-50 achieved 95.00% and 95.56% classification accuracy, respectively, while GooGleNet achieved slightly better results of 96.38%. In contrast, the accuracy of RCNN-DC is 1.67% higher than that of AlexNet, while the number of FLOPs is reduced by 17.2 times, and the accuracy is 1.09% higher than that of ResNet-50, while the number of FLOPs is reduced by 99.7 times, and the accuracy is higher than GooGleNet 0.29% while FLOPs whose number has been reduced by 36.5 times.

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Section: Recognition Methods Of Recursive Attention Model Formentioning

confidence: 99%

Casting Defect Detection and Classification of Convolutional Neural Network Based on Recursive Attention Model

Zhao

2022

Scientific Programming

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“…Among them, a lot of studies [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ] have focused on the inspection of solder joints, which is both important and challenging. Researchers have proposed a number of methods for solder joint inspection that use neural networks [ 1 , 2 ], fuzzy rules [ 3 ], Boolean rules [ 4 ], deep learning [ 5 ], support vector machines [ 6 ], decision trees [ 7 ], principle component analysis [ 8 ], modal analysis [ 9 ], etc. Component placement inspection is another significant and challenging problem, and it is the basis of other PCB inspections, such as the inspection of solder joints.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many researchers have carried out studies on PCB inspection based on machine vision. Among them, a lot of studies [1][2][3][4][5][6][7][8][9][10][11] have focused on the inspection of solder joints, which is both important and challenging. Researchers have proposed a number of methods for solder joint inspection that use neural networks [1,2], fuzzy rules [3], Boolean rules [4], deep learning [5], support vector machines [6], decision trees [7], principle component analysis [8], modal analysis [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of a Printed Circuit Board for Component Recognition Based on Depth Images

Liu

Chen

et al. 2020

Sensors

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Locating and identifying the components mounted on a printed circuit board (PCB) based on machine vision is an important and challenging problem for automated PCB inspection and automated PCB recycling. In this paper, we propose a PCB semantic segmentation method based on depth images that segments and recognizes components in the PCB through pixel classification. The image training set for the PCB was automatically synthesized with graphic rendering. Based on a series of concentric circles centered at the given depth pixel, we extracted the depth difference features from the depth images in the training set to train a random forest pixel classifier. By using the constructed random forest pixel classifier, we performed semantic segmentation for the PCB to segment and recognize components in the PCB through pixel classification. Experiments on both synthetic and real test sets were conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that our method can segment and recognize most of the components from a real depth image of the PCB. Our method is immune to illumination changes and can be implemented in parallel on a GPU.

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High-precision Berenger modes of dual-layer micro-waveguides terminated with a perfectly matched layer for on-chip optical interconnections

Zhu

2013

Microelectronics Reliability

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Defect detection of flip-chip solder joints using modal analysis

Cited by 7 publications

References 25 publications

Casting Defect Detection and Classification of Convolutional Neural Network Based on Recursive Attention Model

Casting Defect Detection and Classification of Convolutional Neural Network Based on Recursive Attention Model

Semantic Segmentation of a Printed Circuit Board for Component Recognition Based on Depth Images

High-precision Berenger modes of dual-layer micro-waveguides terminated with a perfectly matched layer for on-chip optical interconnections

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