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

Nuanprasert, Somchai; Baba, Sueki; Suzuki, Takashi

doi:10.1109/iecon.2015.7392610

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…However, the proposed method did not show a solution if the actual voids in the final check were not of circular shape (e.g. irregular or any complex shape) [330]. Another study conducted by Van Veenhuizen in [208] to inspect voids in solder balls as well; however, deep learning model were used for the defect classification process.…”

Section: ) Segmentationmentioning

confidence: 96%

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: ) Segmentationmentioning

confidence: 96%

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

2020

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“…X-ray is one method where a void(s) in a carrier chip can be observed without destroying the chip (Peng and Do Nam, 2011), and two-dimensional X-ray imaging is used to obtain the object’s geometrical properties. Proprietary geometric technology uses triangulation and periodic lattice unit verification means (Nuanprasert et al , 2015). This technique transforms the ball grid array (BGA) solder joint’s circular region to detect the carrier chip’s void.…”

Section: Introductionmentioning

confidence: 99%

Deep learning and analytical study of void regional formation in flip-chip underfilling process

Ling,

Azahari,

Abas

et al. 2023

SSMT

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Purpose This paper aims to study the relationship between the ball grid array (BGA) flip-chip underfilling process parameter and its void formation region. Design/methodology/approach A set of top-down scanning acoustic microscope images of BGA underfill is collected and void labelled. The labelled images are trained with a convolutional neural network model, and the performance is evaluated. The model is tested with new images, and the void area with its region is analysed with its dispensing parameter. Findings All findings were well-validated with reference to the past experimental results regarding dispensing parameters and their quantitative regional formation. As the BGA is non-uniform, 85% of the test samples have void(s) formed in the emptier region. Furthermore, the highest rating factor, valve dispensing pressure with a Gini index of 0.219 and U-type dispensing pattern set of parameters generally form a lower void percentage within the underfilling, although its consistency is difficult to maintain. Practical implications This study enabled manufacturers to forecast the void regional formation from its filling parameters and array pattern. The filling pressure, dispensing pattern and BGA relations could provide qualitative insights to understand the void formation region in a flip-chip, enabling the prompt to formulate countermeasures to optimise voiding in a specific area in the underfill. Originality/value The void regional formation in a flip-chip underfilling process can be explained quantitatively with indicative parameters such as valve pressure, dispensing pattern and BGA arrangement.

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“…The X-ray imaging detection system has been widely used in the fields of medical diagnosis [1], industrial inspection [2], or nuclear security surveillance [3], etc. Generally speaking, the X-ray cannot be clearly observed by the ordinary imaging sensors, thus some indirect methods are developed [4].…”

Section: Introductionmentioning

confidence: 99%

Calibration of Imaging Sensor and Fiber Optic Taper-Caused Distortion in an X-Ray Intensified CMOS Camera

et al. 2019

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An integrated calibration method of the imaging sensor and the fiber optic taper-caused distortion in an intensified Complementary Metal-Oxide-Semiconductor (CMOS) camera for the X-ray capture application is proposed. Generally, four kinds of distortions need to be corrected in this system: the column noise, the inconsistent pixel intensity response of whole frame, the nonlinear intensity response of each pixel, and the geometry distortion of whole frame. To conquer these problems, first, the column noise is estimated and subtracted from the captured image. The multiple measurement data processing method and the strip noise estimation technique are used. Second, a kind of two-step correction technique of the pixel intensity response is proposed to solve middle two distortion problems. The first step builds the ideal intensity response line for all pixels in image; and the second step estimates and compensates the correction error for each pixel by a polynomial function. Third a polynomial fitting method is also utilized to correct the geometry distortion. The RANdom SAmple Consensus (RANSC) computation mechanism is used to improve the calibration precision further. The optic indices of fiber optic taper and camera are considered to assist the selection of key parameters of these calibration method above. Many experiment results have proved the correctness and effectiveness of proposed method.INDEX TERMS Distortion correction, X-ray image sensor, CMOS, fiber optic taper, RANSAC.

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

Cited by 10 publications

References 10 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

Deep learning and analytical study of void regional formation in flip-chip underfilling process

Calibration of Imaging Sensor and Fiber Optic Taper-Caused Distortion in an X-Ray Intensified CMOS Camera

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