IC Solder Joint Inspection via Robust Principle Component Analysis

Cai, Nian; Zhou, Yang; Ye, Qian; Liu, Gen; Wang, Han; Chen, Xindu

doi:10.1109/tcpmt.2016.2638503

Cited by 30 publications

(31 citation statements)

References 24 publications

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“…This method overcomes similar nonlinear dimensionality methods such as KPCA in terms of the reduced computational time. Cai et al in [174] proposed a modified PCA technique to inspect IC solder joint defects called Robust Principle Component Analysis (RPCA). Their procedure starts by decomposing a set of qualified IC joints images and tested samples of IC solder joints images into low-rank component and an error component using RPCA.…”

Section: ) Model-based Feature Extractionmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Section: ) Model-based Feature Extractionmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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“…The mechanical connection of microwave circuit interconnection should have good reliability, which is a prerequisite for excellent interconnection signal transmission performance. In [13], a robust principle component analysis (RPCA) based inspection model for the appearance of integrated circuit solder joints has been proposed, and the solder joint quality was evaluated accordingly. Wang, et al [14] has developed a three-dimensional electromagnetic field model and a distributed circuit model of bonding wire, and combined with an experimental test, the impact of bonding wire interconnection failure on signal transmission in an a radio frequency (RF) circuit was studied.…”

Section: Introductionmentioning

confidence: 99%

Electromechanical Coupling Parameter Identification for Flexible Conductor Wire Interconnection Considering Interaction Effect in Microwave Circuits

et al. 2021

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With the huge requirement of high frequency, multi-function and high reliability, the quality of microwave circuit interconnection has become an important factor that significantly affects the improvement of microwave electronic system performance. This paper has presented an identification method for flexible conductor wire interconnection (FCWI) electromechanical coupling parameters in microwave circuits with the consideration of their interaction effect. First, a parametric characterization cascade function has been proposed to design the FCWI, and consequently, a three-dimensional electromagnetic structure model of FCWI has been developed and verified. In order to identify the electromechanical coupling parameters of the flexible interconnection considering the interaction effect effectively, this paper has used the range multi-objective function to select the optimal level of the configuration parameter of the flexible interconnection that affects the signal transmission loss. Based on the variance analysis and range analysis of the experimental results, the comprehensive judgment criterion of electromechanical coupling parameters of flexible interconnection can be defined, and therefore, the calculation of electromechanical coupling degree can be derived and the electromechanical coupling property identification of flexible interconnection has been obtained. An example has been used afterwards to verify the accuracy of the proposed method. The method proposed in this paper can be a promising tool for microwave circuit comprehensive design and the optimization of its interconnection, considering both mechanical reliability and electrical performance.

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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%

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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IC Solder Joint Inspection via Robust Principle Component Analysis

Cited by 30 publications

References 24 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electromechanical Coupling Parameter Identification for Flexible Conductor Wire Interconnection Considering Interaction Effect in Microwave Circuits

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

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