Machine Vision-Based Defect Detection in IC Images Using the Partial Information Correlation Coefficient

Wang, Chien-Chih; Jiang, Bernard C.; Lin, Jing-You; Chu, Chien-Cheng

doi:10.1109/tsm.2013.2261566

Cited by 50 publications

(19 citation statements)

References 13 publications

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“…Golden fingers [153]- [155] Traces & Inner layers [156], [192]- [194] Marking inspection [145]- [147], [181] Cosmetic & small defects [31], [194] Scratches and improper etching [195] Glue quality [196] Flux Cutting [181] Alignment and position inspection of PCB [29], [197] PCB Overall quality [188], [198]- [200] Ball Grid Array (BGA) [136], [157], [188], [201]- [209] electronics visual displays due to the variety of their applications ranging from smartphones, tablets, computer monitors, televisions (TVs), to data projectors. LCDs as the name states are made from liquid crystals, which are considered non-emissive materials and therefore does not emit light.…”

Section: Othermentioning

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: Othermentioning

confidence: 99%

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

2020

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“…Vargha et al (2013) discussed interpretation problems of the partial correlation with nonnormally distributed variables. Wang et al (2013) using the partial information correlation coefficient (PICC) method to effectively reduce false alarms in defect detection. Koesterke et al (2014) using the optimized partial correlation coefficient with information theory algorithm to analyze the discovery of biological networks on Stampede's Xeon and Xeon Phi processors.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries

Han

Geng

et al. 2015

Engineering Applications of Artificial Intelligence

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“…These approaches are also used in the semiconductor industry. Wang et al [2] used the statistical approach by proposing the partial information correlation coefficient (PICC) to improve the traditional normalized cross correlation coefficient (TNCCC) for defect detection. They subtracted the standard image from the inspection image to obtain the grayscale difference, calculated the PICC, and compared the PICC with a threshold.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection in Striped Images Using a One-Dimensional Median Filter

Lee

Tai

2020

Applied Sciences

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Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.

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Machine Vision-Based Defect Detection in IC Images Using the Partial Information Correlation Coefficient

Cited by 50 publications

References 13 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

Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries

Defect Detection in Striped Images Using a One-Dimensional Median Filter

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