Automatic detection of Mura defect in TFT-LCD based on regression diagnostics

Fan, Shu-Kai S.; Chuang, Yu-Chiang

doi:10.1016/j.patrec.2010.07.013

Cited by 32 publications

(11 citation statements)

References 11 publications

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“…The detection rate of Mura defects achieved in this study was over 90.9% and the number of defects and the achieved rate of defects on each panel may reach 100%. In another similar study, Fan and Chuang in [247] proposed Mura defect detection algorithm based on regression diagnostics. The sample images were converted to gray-level data, this process was accelerated by dividing the image into several sub-images.…”

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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“…2 (c1) -(c6) show that TFT-LCD often has defects such as color difference, uneven ring, uneven gravity, etc. during the production process [21].…”

Section: A Objectsmentioning

confidence: 99%

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Ming

Cao

Zhang³

et al. 2021

IEEE Access

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Based on the rapid development of semiconductors, integrated circuits and the Internet. 3C products such as computers, tablets, mobile phones and smart TVs have become an indispensable part of people's lives. With the prosperity and development of the 3C product market, the demand for the quality of display panels and related detection technologies are increasing. As the iconic network of deep learning, has been extensively studied in the field of image recognition and defect detection. Based on the development of CNN, this article summarizes the defect detection method of 3C products by CNN with different depths. First, we reviewed the origin of CNN and its structural components, then introduced the upgrade and improvement of important components, and finally introduced and compared the applications of CNN with different depths in defect detection. Through the comparison and summary of the effect of defect detection, we analyze the opportunities and challenges of different CNN frameworks, and exhibit the strategies for different application scenarios.

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“…4 shows low frequency component of segmented image which has defect is larger than that of segmented image which has not defect. The specific value th is defined as follow th m W γ = × × (4) γ =0.01, m is gray level mean of the segmented image and W is window size 128. The constant value γ is obtained experimentally.…”

Section: Defect Detection Algorithmmentioning

confidence: 99%

Mura Region Detection by Using 2D FFT withExponential Kernel for Black Resin-Coated Steel

Kwon¹

2012

IJIEE

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This paper proposes mura region detection algorithm by using two-dimensional fast Fourier transform (2D FFT) with exponential kernel for black resin-coated steel. If the mura exists in the black resin-coated steel image, the image has large low-frequency component. To improve accuracy, multiply exponential kernel to low frequency region. The simulation results show improved performance.Index Terms-2D FFT, defect detection, black resin-coated steel, mura.

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Automatic detection of Mura defect in TFT-LCD based on regression diagnostics

Cited by 32 publications

References 11 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

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Mura Region Detection by Using 2D FFT withExponential Kernel for Black Resin-Coated Steel

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