An improved pattern match method with flexible mask for automatic inspection in the LCD manufacturing process

Lin, Chern‐Sheng; Huang, Kuo-Hon; Lay, Yun-Long; Wu, Kuo‐Chun; Wu, Yieng-Chiang; Lin, Jim‐Min

doi:10.1016/j.eswa.2008.01.035

Cited by 7 publications

(2 citation statements)

References 4 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%

“…The extracted features are then sent to a classifier to decide the source of the defect. Lin et al in [274] suggested a modified pattern matching method to inspect and measure the movement of anisotropic conductive particles. The study used pattern setup and analysis to add a matrix of miscellaneous gray scale to an adaptive one and run a miscellaneous algorithm on part of the pattern block to improve the executive efficacy of the system.…”

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

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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