Research and Development of Intelligent On-Line Real-time Defect Inspection System for Polymer Polarizer

Kuo, Chung‐Feng Jeffrey; Chiu, Chin-Hsun; Chou, You-Chen

doi:10.1080/03602550802634501

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…Table 7 summarizes the defects investigated in literature using AOI system for different FPD types. [250] Mura defects [251]- [256] TFT-LCD panel defects [218], [257], [258] TFT-LCD panel micro-defects such as pinholes, scratches, particles and fingerprints [29], [223], [259], [260] Polarising film defects [261]- [263] Glass substates defects in TFT-LCD [264] Defects during photolithography process [231], [232], [265]- [267] Backlight defects [212], [213], [268], [269] GE operation defects during TFT array process [270], [271] SD operation defects during TFT array process [272], [273] Color filter defects [214] TFT array defects such as fibre defect, particle defect, pattern damage, pattern residual and pattern scratch [274]- [277] Anisotropic Conductive Film defects [278] Optical thin film defects [279] TFT-LCD pad area defects [280] LCD surface deformation for smartphones [224], [225], [225]- [227] Polarizer transparent microdefect [230] Liquid resin defects [217] Subpixel (dots) functional defects OLED [235], [236] Directional textured surface defects in OLED and PLED …”

Section: Othermentioning

confidence: 99%

“…However, the computational time consumption was the worst among them all with 200ms to inspect 500 solder balls. Kuo et al in [259] proposed an optical inspection approach to highlight various kind of defects in polarising films of LCDs such as dust, foreign object, scars of hit, bubble and scratch defects. Various image processing and feature extraction techniques were used for this purpose.…”

Section: D: Hough Transformmentioning

confidence: 99%

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

Ebayyeh

Mousavi

2020

IEEE Access

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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%

Section: D: Hough Transformmentioning

confidence: 99%

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

Ebayyeh

Mousavi

2020

IEEE Access

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“…Lee et al [2] reported a three-phase framework for embedding data mining and machine learning techniques for TFT-LCD manufacturing process. Kuo et al [3] used a linear-array charge-coupled device camera to detect spot and line defects on polarizers. Kuo et al [4] reported a neural network method to recognize six kinds of microdefects of color filters.…”

Section: Introductionmentioning

confidence: 99%

Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

Deng¹,

Pan²,

Zhong³

2020

Applied Sciences

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Featured Application: The paper provides a convenient method to determine if a suspected microdefect of polarizer film is real. This is useful for manufacturing of diverse flexible/ transparent thin-film. Abstract:In the industry of polymer film products such as polarizers, measuring the three-dimensional (3D) contour of the transparent microdefects, the most common defects, can crucially affect what further treatment should be taken. In this paper, we propose an efficient method for estimating the 3D shape of defects based on regression by converting the problem of direct measurement into an estimation problem using two-dimensional imaging. The basic idea involves acquiring structured-light saturated imaging data on transparent microdefects; integrating confocal microscopy measurement data to create a labeled data set, on which dimensionality reduction is performed; using support vector regression on a low-dimensional small-set space to establish the relationship between the saturated image and defects' 3D attributes; and predicting the shape of new defect samples by applying the learned relationship to their saturated images. In the discriminant subspace, the manifold of saturated images can clearly show the changing attributes of defects' 3D shape, such as depth and width. The experimental results show that the mean relative error (MRE) of the defect depth is 3.64% and the MRE of the defect width is 1.96%. The estimation time consumed in the Matlab platform is less than 0.01 s. Compared with precision measuring instruments such as confocal microscopes, our estimation method greatly improves the efficiency of quality control and meets the accuracy requirement of automated defect identification. It is therefore suitable for complete inspection of products.

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“…Utilizing LED illumination sources, Kim et al designed a system for inspecting multiple defects such as bubbles, pits, threads, and alien substances, and they employed an image segmentation and template-matching algorithm to process the acquired images [ 5 ]. Chou et al also developed a set of online real-time defect inspection systems for polymer polarizers, and complex algorithms such as down-sampling compression, wavelet transform, and Hough transform were applied to analyze the images [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Saturated Imaging for Inspecting Transparent Aesthetic Defects in a Polymeric Polarizer with Black and White Stripes

Chen

Zhong

et al. 2018

Materials

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Machine vision systems have been widely used in industrial production lines because of their automation and contactless inspection mode. In polymeric polarizers, extremely slight transparent aesthetic defects are difficult to detect and characterize through conventional illumination. To inspect such defects rapidly and accurately, a saturated imaging technique was proposed, which innovatively uses the characteristics of saturated light in imaging by adjusting the light intensity, exposure time, and camera gain. An optical model of defect was established to explain the theory by simulation. Based on the optimum experimental conditions, active two-step scanning was conducted to demonstrate the feasibility of this detection scheme, and the proposed method was found to be efficient for real-time and in situ inspection of defects in polymer films and products.

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Research and Development of Intelligent On-Line Real-time Defect Inspection System for Polymer Polarizer

Cited by 11 publications

References 9 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

Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

Saturated Imaging for Inspecting Transparent Aesthetic Defects in a Polymeric Polarizer with Black and White Stripes

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