Stereo Vision Based Automated Solder Ball Height and Substrate Coplanarity Inspection

Li, Jinjin; Bennett, Bonnie L.; Karam, Lina J.; Pettinato, Jeff

doi:10.1109/tase.2015.2403836

Cited by 15 publications

(12 citation statements)

References 14 publications

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“…However, the setup procedure is laborious and prone to human error. Li et al in [206] proposed a setup for detecting the height [169] of solder balls in BGA. They used stereo vision system to capture the 3D nature of the solder ball using two CCD cameras at two opposing angles and two ring lights around each camera lens that captures two 2D images in parallel as shown in Figure 27.…”

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

“…When the capacitor reaches the specified position, a laser tube will number the capacitor and produce trigger signal. Industrial camera captures the surface image of this capacitor and optical inspection algorithms are applied to detect the surface defects [206] in real time. Park and Kweon in [234] proposed four image FIGURE 28.…”

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

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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: B Camera/lens Selection and Positioningmentioning

confidence: 99%

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

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

2020

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“…Next, the defect detection between solder paste printing and pick-and-place checks the quality of the solder paste printed on bare PCBs. The quality of solder paste is assessed by measuring the volume, the shape, the coplanarity of solder pastes on each pad [16], [17], [18]. The defect detection at the last stage inspects the quality of solder joints in two steps, where a solder joint is a solidified solder connecting a pad and a component in a PCB.…”

Section: A Defect Detection In Smt Productsmentioning

confidence: 99%

D³PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology

et al. 2020

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In the field of surface mount technology (SMT), early detection of defects in production machines is crucial to prevent yield reduction. In order to detect defects in the production machine without attaching additional costly sensors, attempts have been made to classify defects in solder paste printers using defective solder paste pattern (DSPP) images automatically obtained through solder paste inspection (SPI). However, since the DSPP images are sparse, have various sizes, and are hardly collected, existing CNNbased classifiers tend to fail to generalize and over-fitted to the train set. Besides, existing studies employing only multi-label classifiers are less helpful since when two or more defects are observed in the DSPP image, the location of each defect can not be specified. To solve these problems, we propose a dual-level defect detection PointNet (D 3 PointNet), which extracts point cloud features from DSPP images and then performs the defect detection in two semantic levels: a micro-level and a macro-level. In the micro-level, a type of printer defect per point is identified through segmentation. In the macro-level, all types of printer defects appearing in a DSPP image are identified by multi-label classification. Experimental results show that the proposed D 3 PointNet is robust to the sparsity and size changes of the DSPP image, and its exact match score was 10.2% higher than that of the existing CNN-based state-of-the-art multi-label classification model in the DSPP image dataset.

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“…The tests of repeatability, bias, and real-time performance were conducted to validate that the proposed system met the requirements of online measurement. Li et al [15] proposed an automatic in-line ball height and coplanarity inspection method based on stereo vision technology to detect the potential connectivity in semi-conductor units. The measurement results were compared with those from the laser-scanning and confocal inspection tools.…”

Section: Introductionmentioning

confidence: 99%

“…There are few researches on the stability and agreement of the stereo vision system. Inspired by [14] and [15], an MCA having the accuracy, agreement, and repeatability is proposed as a means to evaluate the position of the needle tip.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Lin,

Shi,

Zhang

et al. 2020

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The precise location of the needle tip is critical in robot-assisted needle-based percutaneous interventions. An automatic needle tip measuring system based on binocular vision technology with the advantages of noncontact, excellent accuracy and high stability is designed and evaluated. First the measurement requirements of the prostate intervention robot are introduced. A laser interferometer is used as the reference for measuring the position of the needle tip whose relative position variation is described as the needle tip distance in the time domain. The parameters of the binocular cameras are obtained by Zhang's calibration method. Then a robust needle tip extraction algorithm is specially designed to detect the pixel coordinates of the needle tip without installing the marked points. Once the binocular cameras have completed the stereo matching, the 3D coordinates of the needle tip are estimated. The measurement capability analysis (MCA) is used to evaluate the performance of the proposed system. The accuracy of the system can be controlled within 0.3621 mm. The agreement analysis is conducted by the Bland-Altman analysis, and the Pearson correlation coefficient is 0.999847. The P/T ratio value is 16.42% in the repeatability analysis. The results indicate that the accuracy and stability of the binocular vision needle tip measuring system are adequate to meet the requirement for the needle tip measurement in percutaneous interventions.

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Stereo Vision Based Automated Solder Ball Height and Substrate Coplanarity Inspection

Cited by 15 publications

References 14 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

D³PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology

Metrology and Measurement Systems

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Stereo Vision Based Automated Solder Ball Height and Substrate Coplanarity Inspection

Cited by 15 publications

References 14 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

D3PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology

Metrology and Measurement Systems

Contact Info

Product

Resources

About

D³PointNet: Dual-Level Defect Detection PointNet for Solder Paste Printer in Surface Mount Technology