• Journal of the Microelectronics and Packaging Society
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• v.13 no.4
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• pp.45-50
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• 2006

Copper via filling is the important factor in 3-D stacking interconnection of SiP (system in package). As the packaging density is getting higher, the size of via is getting smaller. When DC electroplating is applied, a defect-free hole cannot be obtained in a small size via hole. To prevent the defects in holes, pulse and pulse reverse current was applied in copper via filling. The holes, $20\and\;50{\mu}m$ in diameter and $100{\sim}190\;{\mu}m$ in height. The holes were prepared by DRIE method. Ta was sputtered for copper diffusion barrier followed by copper seed layer IMP sputtering. Via specimen were filled by DC, pulse and pulse-reverse current electroplating methods. The effects of additives and current types on copper deposits were investigated. Vertical and horizontal cross section of via were observed by SEM to find the defects in via. When pulse-reverse electroplating method was used, defect free via were successfully obtained.

• Journal of the Korea Safety Management & Science
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• v.19 no.1
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• pp.15-25
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• 2017

The findings were summarized as follows. The safety check by manufacturer showed that 6 of 13 companies are over the average occurrence of defects. It was expected that there would be a difference between manufacturing technology capability and production system of each manufacturer. Consequently, manufacturers should institutionally improve and strengthen certification items for the upward standardization of safety certification before factory. Second, the safety check by year showed that the results of this study accord with those of previous studies on defect time. Consequently, manufacturers should classify the 3-year-old equipment for vehicle-mounted MEWP into a special check subject to do a nondestructive test according to proven results, and also reflect the test in a safety test system to do regular preventive activities of equipment defects. Third, the safety check by part showed that the boom and outrigger parts of vehicle-mounted MEWP have the most defects. Stress concentration resulted in defects as the boom part was most frequently operated in the structural parts for a real work. To prevent this, it is suitable to improve the hardness of boom materials. The outrigger part needs improvement in safety devices with materials. As an outrigger supports the overturning moment of equipment, it is most affected by its load based on the operating radius, resulting in fatigue crack.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• Korean Journal of Materials Research
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• v.21 no.12
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• pp.644-649
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• 2011

[ $A_{3-2x/3}Al_{1-z}In_{z}O_4F:Eu_x^{3+}$ ](A = Ca, Sr, Ba, x = -0.15, z = 0, 0.1) oxyfluoride phosphors were simply prepared by the solid-state method at $1050^{\circ}C$ in air. The phosphors had the bright red photoluminescence (PL) spectra of an $A_{3-2x/3}Al_{1-z}In_{z}O_4F$ for $Eu^{3+}$ activator. X-ray diffraction (XRD) patterns of the obtained red phosphors were exhibited for indexing peak positions and calculating unit-cell parameters. Dynamic excitation and emission spectra of $Eu^{3+}$ activated red oxyfluoride phosphors were clearly monitored. Red and blue shifts gradually occurred in the emission spectra of $Eu^{3+}$ activated $A_3AlO_4F$ oxyfluoride phosphors when $Sr^{2+}$ by $Ca^{2+}$ and $Ba^{2+}$ ions were substituted, respectively. The concentration quenching as a function of $Eu^{3+}$ contents in $A_{3-2x/3}AlO_4F:Eu^{3+}$ (A = Ca, Sr, Ba) was measured. The interesting behaviors of defect-induced $A_{3-2x/3}Al_{1-z}In_{z}O_{4-{\alpha}}F_{1-{\delta}}$ phosphors with $Eu^{3+}$ activator are discussed based on PL spectra and CIE coordinates. Substituting $In^{3+}$ into the $Al^{3+}$ position in the $A_{3-2x/3}AlO_4F:Eu^{3+}$ oxyfluorides resulted in the relative intensity of the red emitted phosphors noticeably increasing by seven times.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.54-61
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• 2001

A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.889-896
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• 2007

Advances in wireless networking, micro-fabrication and integration, and embedded microprocessors have enabled a new generation of massive-scale sensor networks. Because each sensor node is limited in size and capacity, it is very important to design a new simple and energy efficient protocol. Among conventional sensor networks' routing protocols, the directed diffusion scheme is widely blown because of its simplicity. This scheme, however, has a defect in that sending interest and exploratory data messages while setting connection paths consumes much energy because of its flooding scheme. Therefore, this paper proposes a new sensor network routing protocol, called sequenced directed diffusion with a threshold control, which compromises the conventional directed diffusion scheme's defect and offers an energy efficient routing idea. With a computer simulation, its performance is evaluated and compared to the conventional directed diffusion scheme. Numerical results show that the proposed scheme offers energy efficiency while routing packets, and resolves ill-balanced energy consumption among sensor nodes.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.28 no.1
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• pp.9-13
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• 2018

In this work, we investigated the variation of optical characteristics with the thickness of bulk GaN grown by hydride vapor phase epitaxy(HVPE) to evaluate applicability as GaN substrates in fabrication of high-brightness optical devices and high-power devices. We fabricated 2-inch GaN substrates by using HVPE method of various thickness (0.4, 0.9, 1.5 mm) and characterized the optical property with the variation of defect density and the residual stress using chemical wet etching, Raman spectroscopy and photoluminescence. As a result, we confirmed the correlation of optical properties with GaN crystal thickness and applicability of high performance optical devices via fabrication of homoepitaxial substrate.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2317-2318
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• 2008

This report is about wireless energy transform system. It means something to charge without a line of contact. Existing charging has been many defect, including badness of the line, limitation of the space and the time and so on But this is one of the way that can complement it. So our goal is making the wireless charger that is effective and easy to use.