A new algorithm on the automatic <scp>TFT‐LCD</scp> mura defects inspection based on an effective background reconstruction

Ngo, Chinh; Park, Yong Jin; Jung, Ji-Young; Hassan, Rizwan Ul; Seok, Jongwon

doi:10.1002/jsid.622

Cited by 23 publications

(22 citation statements)

References 16 publications

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“…As these Mura defect always has no distinct difference from background region in both intensity and gradient, simple image processing methods for detection are infeasible [219]. In general, Mura assessment is performed by observing any imperfections present on the scale of a few pixels to usually less than 20% of the screen diagonal and not on a large area [220]. Researchers in [221], [222] investigated one of the assembly processes that may produce gap Mura defect in TFT-LCD.…”

Section: Othermentioning

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%

“…Despite of these advantages this method cannot detect defects with sub-pixel accuracy [315]. Ngo et al in [220] performed background reconstruction to extract the features of different mura defects in TFT-LCD. Several algorithms were used for this purpose such as DCT, polynomial surface fitting and segmentation.…”

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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“…Lin et al [6] presented an image processing method for defect detection in TFT-LCD images and used genetic algorithm (GA) for adjusting heuristics automatically. Ngo et al [7] also presented an automatic detection method for MURA by accurate reconstruction of the background by training separately on the background but using test set images of MURA. In non-ML based method, Du-Ming Tsai et al [8] used Fourier transform based technique to remove the repeated patterns in background and then used adaptive threshold to perform defect segmentation.…”

Section: Literature Survey For Mura Defect Inspectionmentioning

confidence: 99%

Deep Learning based MURA Defect Detection

Singh¹,

Kumar²,

Sultania³

et al. 2019

EAI Endorsed Transactions on Cloud Systems

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INTRODUCTION: MURA defects in LED/LCD panels are one of the most challenging defects for Automatic Defect Classification and Localization (ADC) due to their extremely low contrast when compared with the background. Manual detection is subjective, error prone, very tedious and time consuming. Even when the type of MURA defects can be ascertained manually, the exact bounding box for defect is hard to determine. Various heuristic based image processing techniques have been applied giving sub-optimal accuracy over generic datasets. OBJECTIVES: The primary objective of this paper is to check whether the state of the art DL (Deep Learning) network for general object classification and localization (MSCOCO PASCAL VOC etc.) can be applied successfully for MURA Defect Classification and Localization. METHODS: In this paper we present a single DL pipeline for classification and localization which for the first time is applied for MURA defects. Naive DL network-Single Shot multi-box Detector (SSD, pre-trained on ImageNet) was not sufficient to give a good F1 score because of the nature of the defect. Accuracy improved a little after applying various DL specific optimization methods such as loss function optimization, network optimization etc. Utilizing the knowledge from MURA domain for data augmentation, like filtering based on image capture wavelength etc. improved the results significantly. RESULTS: Using optimization techniques that are from both DL domain as well as specific to MURA domain, we show improvement in the accuracy of the base DL pipeline from ~30% to ~80%. Minimum heuristics were used to define the pipeline so that it can easily adapt to any new MURA dataset. The paper shows the importance of domain specific preprocessing steps for the designed network in case of MURA defects. CONCLUSION: Using DL, MURA classification and localization had not been tried before. For the first time we demonstrated results for both classification and localization of MURA defects using state-of-the-art DL network with F1~80%. We also conclude that state-of-the-art network for general object detection can be reused with the help of Transfer Learning (TL) concept and fine-tuned with MURA domain specific optimizations mentioned in paper for optimal performances in MURA domain.

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“…Another Mura detection method is based on background reconstruction [6,[11][12][13][14][15][16][17][18][19][20][21]. This kind of method firstly reconstructs the background of an image, and then obtains a residual image which contains Mura information by subtracting the background image from the original one.…”

Section: Introductionmentioning

confidence: 99%

A Mura Detection Method Based on an Improved Generative Adversarial Network

Xie

Yang

Wang³

et al. 2021

IEEE Access

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Mura is defined as visual unevenness on the display panel. It can cause unpleasant feelings, so it is necessary to perform Mura inspection during the display quality test. However, Mura is quite difficult to be detected because of its irregular shape and size as well as its low contrast. To solve this practical problem, we proposed a GAN-based model named UADD-GAN to detect Mura in this work. Consisting of a proposed UADD generator and a discriminator, the model is trained using only normal samples, after which the generator is able to simulate the distribution of normal samples. During training, the generator takes normal images as inputs and outputs their reconstructions, while the discriminator receives images and determines whether they are original or reconstructed ones, defiantly helping the generator to perform reconstructions better. The symmetric structure and the skip-adding operation make it easy for the UADD generator to reconstruct the normal samples well. In the detecting procedure, the generator performs worse in the reconstruction of samples with Mura so that we can distinguish them from the normal ones. To make full use of discriminator, we use multiple feature layers of the discriminator for supervision instead of using only the classification layer, helping the generator to reconstruct normal samples better. Meanwhile, a two-side detecting method was used to detect Mura since all the samples are not in a square shape and it greatly improved the detecting accuracy. We have conducted experiments of Mura data sets with different proportion and our research indicates that our proposed method surpasses other state of the art methods.

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A new algorithm on the automatic TFT‐LCD mura defects inspection based on an effective background reconstruction

Cited by 23 publications

References 16 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

Deep Learning based MURA Defect Detection

A Mura Detection Method Based on an Improved Generative Adversarial Network

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