Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing

Yousefian-Jazi, Ali; Ryu, Jun-Hyung; Yoon, Seongkyu; Liu, J. Jay

doi:10.1016/j.jprocont.2013.12.009

Cited by 24 publications

(21 citation statements)

References 34 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%

“…For inspecting glass substrate defects in TFT-LCD, cameras are aligned in two different positions: transmission and reflection. In transmission position the camera captures the projected images with light transmitting through the glass whereas in the reflection position it captures the reflected images with light reflecting off the substrates as shown in Figure 25 [263]. Hence, Yousefian-Jazi et al in [263] used the transmission method for inspecting TFT-LCD glass substrates.…”

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

“…In transmission position the camera captures the projected images with light transmitting through the glass whereas in the reflection position it captures the reflected images with light reflecting off the substrates as shown in Figure 25 [263]. Hence, Yousefian-Jazi et al in [263] used the transmission method for inspecting TFT-LCD glass substrates. Similar techniques are considered when inspecting highly reflective surfaces, in which certain geometrical laws must be applied to measure the optimum lengths and angles of the camera and illumination settings [311].…”

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

“…However, considering that this is a large number of features to be extracted, this may affect the inspection speed. Youesefian-Jazi et al in [263] used multiple feature extraction techniques to extract surface flaws and scratch defects in TFT-LCD glass substrates. To reduce unwanted noise from illumination, DWT were first used on each glass substrate sub-image.…”

Section: D: Hough Transformmentioning

confidence: 99%

“…The other way is using data preprocessing techniques such as sampling, in which either new samples are added, or existing samples are removed from the original data. The process of removing samples is known as undersampling and the process of adding new samples is known as over-sampling [263]. Hence, GAN and Bootstrapping can be used to solve the data imbalance problem as well.…”

Section: ) Machine Learning Classifiersmentioning

confidence: 99%

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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%

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

Section: B Camera/lens Selection and Positioningmentioning

confidence: 99%

Section: D: Hough Transformmentioning

confidence: 99%

Section: ) Machine Learning Classifiersmentioning

confidence: 99%

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

2020

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Defect detection in automotive glass based on modified YOLOv5 with multi-scale feature fusion and dual lightweight strategy

Chen,

Huang,

et al. 2024

Vis Comput

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A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction

et al. 2018

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Manufacturing quality prediction model, as an effective measure to monitor the quality in advance, has been developed using various data-driven techniques. However, multi-parameter in multi-stage of the modern manufacturing industry brings about the curse of dimensionality, leading to the difficulties for feature extraction, learning and quality modeling. To address this issue, three dimension reduction techniques are investigated in this paper, i.e., principal component analysis (PCA), locally linear embedding (LLE), and isometric mapping (Isomap). Specifically, the PCA is a linear dimension reduction technique, the LLE is a nonlinear reduction technique with local perspective, and the Isomap is a nonlinear reduction technique from global perspective. After getting the low-dimensional information from the PCA, the LLE, and the Isomap methods respectively, a support vector machine (SVM) is utilized for modeling. To reveal the effectiveness of the dimension reduction techniques and compare the difference of the three dimension reduction techniques, two experimental manufacturing data are collected from a competition about manufacturing quality control in Tianchi Data Lab of China. The comparison experiments indicate that the dimension reduction techniques have capacity for improving the SVM modeling performance indeed, and the Isomap-SVM model with the nonlinear global dimension reduction outperforms all the candidate models in terms of qualitative and quantitative analysis.

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Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing

Cited by 24 publications

References 34 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

Defect detection in automotive glass based on modified YOLOv5 with multi-scale feature fusion and dual lightweight strategy

A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction

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