Automated Detection and Classification of Non-Wet Solder Joints

Said, Asaad F.; Bennett, Bonnie L.; Karam, Lina J.; Pettinato, Jeffrey S

doi:10.1109/tase.2010.2043097

Cited by 18 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 10 summarizes the other image acquisition techniques and auxiliary systems used for AOI in literature. Table 4 SEM [111], [113], [117], [126], [127], [134], [135], [318] [44] [315] OCT [230], [278], [313], [314] Thermography [209], [319] X-ray [197], [204], [205], [208], [319]- [321]…”

Section: Auxiliary Systems and Other Image Acquisition Techniquesmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

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.

show abstract

Section: Auxiliary Systems and Other Image Acquisition Techniquesmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

show abstract

“…This procedure allows us to classify different defects in to known loss codes based on a set of well-defined features for each defect. In the proposed method, we were able to come up with a reference-free approach that is based on analyzing a set of significant features [5,11] to filter out suspect artifacts leaving only the desired defects. The feature parameters used in our reference-free method are based on using histogram techniques, detecting and comparing the characteristics of neighboring artifacts, and analyzing the shape and size of the artifact in consideration.…”

Section: B Feature Analysismentioning

confidence: 99%

“…This paper focuses on automatic defect detection in the bare die units using digital image processing. Digital image processing is being used in many industrial applications in order to help solving real problems using consistent, accurate, and fast solution [1][2][3][4][5][6][7]. Die area exhibits different types of defects.…”

Section: Introductionmentioning

confidence: 99%

Die level defects detection in semiconductor units

Said

Patel

2013

ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference

Self Cite

View full text Add to dashboard Cite

The assembly test process has many steps where defects can be created at any time during different stages. Detecting defects at early stages is very crucial and saves a lot of cost and time by isolating the defective parts from further processing. Detecting defects on the die area of the semiconductor units is a challenging procedure due to the fact that die's defects exhibits large variations in intensity and shape. The existing manual and automated inspection approaches still produce high rate of under-rejection and over-rejection which impacts the yield and adds significant cost to the inspected unit. A robust die level defect detection procedure is presented in this paper in order to come up with a solution that is cheaper, easier to sustain, and that would automatically inspect each unit for defects providing for efficient baseline characterization and rapid excursion detection. The proposed method gives a higher detection rate of die level defects with under-rejecting and overrejection rates within the acceptable criteria.

show abstract

“…And recently, the solder ball size and the spacing between pitches have been continuously reduced in order to achieve a demand for higher density package in manufacturing, because of this; the qualities of solder joints assembly are naturally concerned. To assure the reliability of solder joints, naturally, we can incorporate several different types of automated inspection methodologies into a screening system; especially, the inspection techniques based on X-ray imaging approaches for identifying, e.g., cracks [1], abnormal joint shapes [2], non-wet soldering [3], and void defects [4], [5], [6], [7], [8] in solder joints, non-destructively, were developed in previous studies. Importantly, the existence of voids at joints may degrade the mechanical strength of the interconnection potentially reducing the performance of electrical conductivity, which can seriously affect the longterm reliability of devices [9], [10].…”

Section: Introductionmentioning

confidence: 99%

An efficient method of occluded solder ball segmentation for automated BGA void defect inspection using X-ray images

Nuanprasert

Baba²,

Suzuki

2015

IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

One challenge concerning the reliability of ball grid array (BGA) packages assembly is to detect the void defects occurring inside solder balls. Additionally, for use in mass production, an automated inspection system has increasingly become an attractive solution. In practice, the first procedure of this system is required to segment the individual solder balls from the background. Here, we have proposed the efficient method to analyze the X-ray images obtained from arbitrary rotating angles of a printed circuit board (PCB). Specifically, we have succeeded in dealing with the general pattern of solder balls arrangement by applying the Delaunay triangulation technique and verifying the unit cell of periodic lattice. The proposed method is robust against occluded balls caused by some interference and outperforms the conventional method in term of yielding higher accuracy of solder ball segmentation and lower processing time. These conclusions were confirmed by our experiments.

show abstract

Automated Detection and Classification of Non-Wet Solder Joints

Cited by 18 publications

References 18 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

Die level defects detection in semiconductor units

An efficient method of occluded solder ball segmentation for automated BGA void defect inspection using X-ray images

Contact Info

Product

Resources

About