• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.86-93
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• 2017

In this paper, we propose a novel defect detection method using component tree representations of scanning electron microscopy (SEM) images. The component tree contains rich information about the topological structure of images such as the stiffness of intensity changes, area, and volume of the lobes. This information can be used effectively in detecting suspicious defect areas. A quasi-linear algorithm is available for constructing the component tree and computing these attributes. In this paper, we modify the original component tree algorithm to be suitable for our defect detection application. First, we exclude pixels that are near the ground level during the initial stage of component tree construction. Next, we detect significant lobes based on multiple attributes and edge information. Our experiments performed with actual SEM wafer images show promising results. For a $1000{\times}1000$ image, the proposed algorithm performed the whole process in 1.36 seconds.

• Journal of the Optical Society of Korea
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• v.19 no.6
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• pp.619-628
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• 2015

In this paper, we consider a method for estimating a stripe-type defect and the reconstruction of a defect-free L/S type mask used in lithography. Comparing diffraction patterns of defected and defect-free masks, we derive equations for the estimation of the location and size of the defect. We construct an analytical model for this problem and derive closed form equations to determine the location and size using phase retrieval problem solving techniques. Consequently, we develop an algorithm that determines a defect-free mask pattern. An example shows the validity of the equations.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.327-334
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• 2024

Bridge inspection is crucial for infrastructure maintenance. Current inspections based on computer vision primarily focus on identifying simple defects such as cracks or corrosion. These detection results can serve merely as preliminary references for bridge inspection reports. To generate detailed reports, on-site engineers must still present the structural conditions through lengthy textual descriptions. This process is time-consuming, costly, and prone to human error. To bridge this gap, we propose a deep learning-based framework to generate detailed and accurate textual descriptions, laying the foundation for automating bridge inspection reports. This framework is built around an encoder-decoder architecture, utilizing Convolutional Neural Networks (CNN) for encoding image features and Gated Recurrent Units (GRU) as the decoder, combined with a dynamically adaptive attention mechanism. The experimental results demonstrate this approach's effectiveness, proving that the introduction of the attention mechanism contributes to improved generation results. Moreover, it is worth noting that, through comparative experiments on image restoration, we found that the model requires further improvement in terms of explainability. In summary, this study demonstrates the potential and practical application of image captioning techniques for bridge defect detection, and future research can further explore the integration of domain knowledge with artificial intelligence (AI).

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.158-164
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• 2005

Kusukawa and Ohta presented the $CS_{CQ-r}$ chart to monitor the process defect $rate{\lambda}$ in high-yield processes that is derived from the count of defects. The $CS_{CQ-r}$ chart is more sensitive to $monitor{\lambda}$ than the CQ (Cumulative Quantity) chart proposed by Chan et al.. As a more superior chart in high-yield processes, we propose a Synthetic chart that is the integration of the CQ_-r chart and the $CS_{CQ-r}$chart. The quality characteristic of both charts is the number of units y required to observe r $({\geq}2)$ defects. It is assumed that this quantity is an Erlang random variable from the property that the quality characteristic of the CQ chart follows the exponential distribution. In use of the proposed Synthetic chart, the process is initially judged as either in-control or out-of-control by using the $CS_{CQ-r}$chart. If the process was not judged as in-control by the $CS_{CQ-r}$chart, the process is successively judged by using the $CQ_{-r}$chart to confirm the judgment of the $CS_{CQ-r}$chart. Through comparisons of ARL (Average Run Length), the proposed Synthetic chart is more superior to monitor the process defect rate in high-yield processes to the stand-alone $CS_{CQ-r}$ chart.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.9
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• pp.73-79
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• 2012

In this paper, we study on how to determine the number of hidden layer neurons in neural network for predicting defect size of steam generator tube. It was reported in the literature that the number of hidden layer neurons can be efficiently determined with the help of cross-validation. Although the cross-validation provides decent estimation performance in most cases, the performance depends on the selection of validation set and rather poor performance may be led to in some cases. In order to avoid such a problem, we propose to use multifold cross-validation. Through the simulation study, it is shown that the estimation performance of defect width (defect depth, respectively) attains 94% (99.4%, respectively) of the best performance achievable among the considered neuron numbers.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.312-322
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• 2015

In this paper a new defect detection method for flat panel display that does not require any separately prepared reference images and shows robustness against problems with regard to pixel tolerance and nonuniform illumination condition is proposed. In order to perform defect detection under any magnification value of camera, the proposed method automatically obtains the value of pattern interval through an image analysis. Using the information for pattern interval, an advanced PCSR-G method presented in this paper utilizes neighboring patterns as its reference images instead of utilizing any separately prepared reference images. Also this paper proposes a scheme to improve the performance of the conventional PCSR-G method by extracting and applying additional information for pixel tolerance and intensity distribution considering the value of pattern interval. Simulation results show that the performance of the proposed method utilizing pixel tolerance and intensity distribution is superior to that of the conventional method. Also, it is proved that the proposed method that is implemented using parallel technique based on GPGPU can be applied to real system.

• Korean Journal of Materials Research
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• v.11 no.9
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• pp.781-785
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• 2001

The thermal behavior of Flow Pattern Defect (FPD) and Large Pit (LP) in Czochralski Silicon crystal was investigated by applying high temperature annealing ($\geq$$1100^{\circ}C$) and non-agitated Secco etching. For evaluation of the effect of LP upon device performance/yield, commercial DRAM and ASIC devices were fabricated. The results indicated that high temperature annealing generates LPs whereas it decreases FPD density drastically. However, the origins of FPD and LP seemed to be quite different by not showing any correspondence to their density and the location of LP generation and FPD extinction. By not showing any difference between the performance/yield of devices whose design rule is larger than 0.35 $\mu\textrm{m}$, LP seemed not to have detrimental effects on the performance/yield.

• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.907-914
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• 2018

In this article, we conducted molecular dynamics simulations to investigate the effect of applied strain and temperature on irradiation-induced damage in alpha-zirconium. Cascade simulations were performed with primary knock-on atom energies ranging between 1 and 20 KeV, hydrostatic and uniaxial strain values ranging from -2% (compression) to 2% (tensile), and temperatures ranging from 100 to 1000 K. Results demonstrated that the number of defects increased when the displacement cascade proceeded under tensile uniaxial hydrostatic strain. In contrast, compressive strain states tended to decrease the defect production rate as compared with the reference no-strain condition. The proportions of vacancy and interstitial clustering increased by approximately 45% and 55% and 25% and 32% for 2% hydrostatic and uniaxial strain systems, respectively, as compared with the unstrained system, whereas both strain fields resulted in a 15-30% decrease in vacancy and interstitial clustering under compressive conditions. Tensile strains, specifically hydrostatic strain, tended to produce larger sized vacancy and interstitial clusters, whereas compressive strain systems did not significantly affect the size of defect clusters as compared with the reference no-strain condition. The influence of the strain system on radiation damage became more significant at lower temperatures because of less annealing than in higher temperature systems.

• Journal of the Optical Society of Korea
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• v.17 no.1
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• pp.50-56
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• 2013

Optical-fiber electronic speckle pattern interferometry (ESPI) is a non-contact, non-destructive examination technique with the advantages of rapid measurement, high accuracy, and full-field measurement. The optical-fiber ESPI system used in this study was compact and portable with the advantages of easy set-up and signal acquisition. By suitably configuring the optical-fiber ESPI system, producing an image signal in a charge-coupled device camera, and periodically modulating beam phases, we obtained phase information from the speckle pattern using a four-step phase shifting algorithm. Moreover, we compared the actual defect size with that of interference fringes which appeared on a screen after calculating the pixel value according to the distance between the object and the CCD camera. Conventional methods of measuring defects are time-consuming and resource-intensive because the estimated values are relative. However, our simple method could quantitatively estimate the defect length by carrying out numerical analysis for obtaining values on the X-axis in a line profile. The results showed reliable values for average error rates and a decrease in the error rate with increasing defect length or pressure.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.