• Proceedings of the Materials Research Society of Korea Conference
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• 2009.05a
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• pp.12.2-12.2
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• 2009

전자 디스플레이 산업의 중요성과 미래사회에서 요구되는 정보기기로써 유연한 기판을 사용한 소자에 대한 수요가 급격히 증가하고 있으며, 이들 산업에 응용되기 위해서는 저비용, 고생산 공정이 요구되고 있다. 이를 위해 인쇄전자 기술에 대한 연구가 활발히 진행되고 있다. 특히, 금속배선은 모든 소자의 기본이면서 낮은 저항과 높은 신뢰성을 동시에 요구하고 있어 인쇄전자 기술이 해결해야 할 가장 어려운 난제 중의 하나이다. 따라서 본 연구에서는 낮은 저항과 높은 신뢰성을 만족시킬 수 있는 새로운 금속배선 공정으로서 폴리이미드 필름을 초발수 처리한 후 친수 패턴을 하여 전도성 잉크에 함침함으로서 친수 패턴을 따라 금속배선이 이루어 지도록 하는 방법을 제안하고자 한다. 폴리이미드 필름의 표면을 플라즈마 처리하여 표면에 나노돌기를 형성시키고 불소기를 함유한 코팅층을 형성시킴으로써 물에 대한 접촉각이 $150^{\circ}$이상이 되도록 초발수 처리할 수 있었다. 초발수 처리된 폴리이미드 기판에 쉐도우 마스크를 사용하여 UV조사함으로써 조사된 부분만 친수성을 가지는 패턴을 형성하였다. 이렇게 친수 패턴이 제작된 초발수 폴리이미드 유연기판을 실버잉크에 함침함으로써 선폭 $200{\mu}m$를 가지는 금속배선을 형성시켰다. 형성된 금속배선의 단면 형상을 측정하였으며, 열처리를 통하여 비저항이 $30{\mu}{\Omega}$-cm를 얻을 수 있었다. 통상 1회의 함침으로는 금속배선의 두께가 150nm정도로 금속배선으로 사용하기에는 얇아 배선의 두께를 증가시키기 위하여 수 회 함침을 시도하여 $2{\mu}m$의 두께로 증가시킬 수 있었다. 이때 선폭과 선간 간격은 크게 변하지 않고 두께만 증가시킬 수 있었다. 이는 금속배선을 형성한 후에도 폴리이미드 유연기판의 초발수성은 그대로 유지되어 여러번 함침할 때 잉크가 이미 형성된 배선에만 묻게 되어 두께는 증가하나 선폭과 선간 간격은 증가하지 않는 것으로 판단된다. 사용한 실버잉크는 실버의 함량은 10~20wt%인 수계 잉크였다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.33-33
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• 2007

In PDP industry, the dielectrics and barrier ribs have been required with low dielectric constant, low melting point and Pb-free composition due to the low power consumption, low signal delay time and the environment restriction. We were studied with $B_2O_3-Al_2O_3$-SrO glass systems about optical, thermal and dielectric properties. The glass forming region of the $B_2O_3-Al_2O_3$-SrO glass systems was narrow due to the amount of the glass former $(B_2O_3)$. The glass transition temperature (Tg) of the glasses was at $550{\sim}590^{\circ}C$. The glasses have 6~8 for the dielectric constant. Furthermore, the transmittance of the glasses was over 80% on the range of the visible ray. From the results, the glasses of the $B_2O_3-Al_2O_3$-SrO glass systems should enable to be a good candidate of the PDP devices for information display with low dielectric constant. The aim of this study is to give a fundamental result of new glass system for low dielectric constant in the information display.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.6
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• pp.506-511
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• 2009

For the application of the photosensitive barrier ribs with optimal properties such as glass transition temperature, refractive index and coefficient thermal expansion, the boro-silicate glasses was studied. The glass transition temperature, coefficient thermal expansion, and refractive index of the glasses based on the $B_2O_3-Al_2O_3-Al_2O_3-SiO_2$ glass system have been investigated with the different ratio of BaO/$Na_2O$ and $B_2O_3/Na_2O$. Increasing the ratio of $B_2O_3/Na_2O$ was led to the increase of coefficient thermal expansion and the decrease of glass transition temperature. The increase of refractive index of boro-silicate glasses increased with the density of glasses. We suggest the empirical equation for the prediction of refractive index with the glass density, $n=0.123{\rho}+1.182$ with 0.042 as the standard deviation in the boro-silicate glass system. The aim of the present paper is to give a basic result of the thermal and optical properties for designing the composition of photosensitive barrier ribs in PDP.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.31-37
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• 2021

In this paper proposed a feature fusion and spatial attention-based modified YOLOv4S for small and occluded detection. Conventional YOLOv4S is a lightweight network and lacks feature extraction capability compared to the method of the deep network. The proposed method first combines feature maps of different scales with feature fusion to enhance semantic and low-level information. In addition expanding the receptive field with dilated convolution, the detection accuracy for small and occluded objects was improved. Second by improving the conventional spatial information with spatial attention, the detection accuracy of objects classified and occluded between objects was improved. PASCAL VOC and COCO datasets were used for quantitative evaluation of the proposed method. The proposed method improved mAP by 2.7% in the PASCAL VOC dataset and 1.8% in the COCO dataset compared to the Conventional YOLOv4S.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.28-34
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• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.27-34
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• 2019

In this paper, we propose a method to remove the background area by analyzing the pattern of the character area. In the character detection result of the MSER(Maximally Stable External Regions) method which distinguishes a region having a constant contrast background regions were detected. To solve this problem, we use the MSER method in natural images, the background is removed by calculating the change rate by searching the character area and the background area which are not different from the areas where the contrast values are different from each other. However, in the background removed image, using the LBP(Local Binary Patterns) method, the area with uniform values in the image was determined to be a character area and character detection was performed. Experiments were carried out with simple images with backgrounds, images with frontal characters, and images with slanted images. The proposed method has a high detection rate of 1.73% compared with the conventional MSER and MSER + LBP method.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.307-313
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• 2021

In this paper, a lightweight network with fewer parameters compared to the existing object detection method is proposed. In the case of the currently used detection model, the network complexity has been greatly increased to improve accuracy. Therefore, the proposed network uses EfficientNet as a feature extraction network, and the subsequent layers are formed in a pyramid structure to utilize low-level detailed features and high-level semantic features. An attention process was applied between pyramid structures to suppress unnecessary noise for prediction. All computational processes of the network are replaced by depth-wise and point-wise convolutions to minimize the amount of computation. The proposed network was trained and evaluated using the PASCAL VOC dataset. The features fused through the experiment showed robust properties for various objects through a refinement process. Compared with the CNN-based detection model, detection accuracy is improved with a small amount of computation. It is considered necessary to adjust the anchor ratio according to the size of the object as a future study.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.