• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.887-890
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• 1997

Recently micromachining using DPSSL(Diode Pumped Solid State Laser) with 3rd harmonic wavelength is actively studied in laser machining area. Micromachining using DPSSL have outstanding advantages as UV source comparing with excimer laser in various aspect such a maintenance cost, maskless machining, high repetition rate and so on. In this study micro-drilling of PCB type substrate which consists of Cu-PI-Cu layer was performed using DPSS Nd:YAG laser(355nm, wavelength) in vector scanning method. Experimental and numerical method(Matlab simulation, FEM) are used to optimize process parameter and control machining depth. The man mechanism of this process is laser ablation. It is known that there is large gap between energy threshold of copper and that of PI. Matlab simulation considering energy threshold of material is performed to effect of duplication of pulse and FEM thermal analysis is used to predict the ablation depth of copper. This study could be widely used in various laser micromachining including via hole microdrilling of PCB, and micromachining of semiconductor components, medical parts and printer nozzle and so on.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.698-704
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• 1999

There are several types of defects of SMDs mounted on PCB, that is, missing components, misalignment, wrong parts and poor solder joints. This research study mainly focuses on measuring of deviation of SMD leads using the partial image of component, not using the full image. This processing based on the partial image has the advantage of the reduction in calculation time compared to the full image. Since position of lead is calculated with respect of the reduction in calculation time compared to the full image. Since position of lead is calculated with respect to pad, the accuracy of the system is not dependent on percise positioning stage. The grabbed image of gray scale is converted into binary format using a cutomatic threshold. After small fragments in the image is removed by a series of morphology operations such as opening and closing, the centroids of PCB pads and SMD leads is obtained together with labeling of blobs. Translational shift and rotationial angle of SMD are succedingly estimated using above information and chip data. The expression that can calculate the maximum deviation of leads with respect to PCB pads has been derived, and inferior mounting of SMD is judged by a given criterion. Some experiments have been executed to verify this measuring scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1089-1095
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• 2010

We propose an image segmentation method for auto-teaching system of PCB (Printed Circuit Board) assembly inspection machines. The inspection machine acquires images of all components in PCB, and then compares each image with its standard image to find the assembly errors such as misalignment, inverse polarity, and tombstone. The component window that is the area of component to be acquired by camera, is one of the teaching data for operating the inspection machines. To reduce the teaching time of the machine, we newly develop the image processing method to extract the component window automatically from the image of PCB. The proposed method segments the component window by excluding the soldering parts as well as board background. We binarize the input image by use of HSI color model because it is difficult to discriminate the RGB colors between components and backgrounds. The linear combination of the binarized images then enhances the component window from the background. By use of the horizontal and vertical projection of histogram, we finally obtain the component widow. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.4
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• pp.37-44
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• 2001

The experimental results and application potentials of a ZVS Forward DC-DC converter based coreless PCB transformer and coreless inductor are presented. The experimental converter, that has a maximum power of 12W, maximum switching frequency of 2.2MHz and nominal input voltage of 24V, has been successfully implemented. The coreless PCB transformer and inductor are found to have many favorable characteristics to high frequency operations due to the absence of a core loss. A power conversion efficiency of the experimental converter was measured at 70${\sim}$80%, and the output was regulated at 12V within 0.7% tolerance.

• The Korean Journal of Ecology
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• v.25 no.6
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• pp.371-377
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• 2002

Leaves of two-week old seedlings of tomato (Lycopersicon esculentum) were treated with various concentrations (0, 0.2 and 0.4 $\mu$g/1) of 2,4,5-trichlorobiphenyl (PCB-29) and subsequent growth of seedlings, symptoms of oxidative stress and activities of antioxidant enzymes were investigated. Compared with the non-treated control, foliar application of PCB-29 decreased both biomass and superoxide ($O_2$) radical production but increased hydrogen peroxide production and lipid peroxidation such as malondialdehyde (MDA) formation with increased activities of superoxide dismutase (SOD), ascorbate peroxidase (APX) and guaiacol peroxidase (GPX). Further studies on the isozymes of SOD, peroxidase (POD) and APX showed that all three isozymes of SOD such as Mn-SOD, Fe-SOD and Cu/Zn-SOD, two among four isozymes of POD and all three isozymes of APX were selectively increased in response to PCB. Therefore, we suggest that a possible cause for the reduction of seedling growth by PCB exposure is the oxidative stress including over production of hydrogen peroxide and the selective expression of specific isozymes of some antioxidant enzymes.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.129-134
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• 2013

In this study, the heat flow analysis compatible commercial code CFX 11 was used to develop the structure inside PCB circuit board devices, which could stable radiant heat as well as the cooling device within it. In case of modifying the arrangement of electronic parts on the PCB inside the multi channel temperature measurement board devices, radiant heat effects did not show a rising tendency, whereas the overall temperature went down in case of installing the vents in the outer case of PCB circuit board devices. In terms of installation location, it was the most appropriate to install it on the electronic parts with no heat. Besides, in case of mounting the fan as a cooling device by considering various user environments for multi channel temperature measurement board devices, the radiant heat effects were shown higher than in case of installing the vents, and the middle sections were the most appropriate to its installation location. In case of changing the wind quantity of the fan from its selected installation location, the best radiant heat effects were shown at high speed as expected.

• Ocean and Polar Research
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• v.29 no.4
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• pp.297-302
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• 2007

Atmospheric levels of polychlorinated biphenyls (PCBs) at the King Sejong station were monitored for one year using passive air samplers. Low-chlorinated PCB homologues were predominant in all samples. PCB levels were observed to decrease with distance from the station, which may indicate that a significant part of PCBs could be of local origin. Although the level of PCBs at the King Sejong station is very low (${\Sigma}_9PCB$ (18, 52, 101, 118, 128, 138, 153, 180, 187): $2.3\;pg\;m^{-3}$) probably due to decrease in the global PCB emissions, it is one order of magnitude higher than a background level in the Antarctic. Based on this preliminary study, more interpretation on PCB data and meteorological conditions is required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.166-169
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• 2017

Currently, studies using Deep Learning are actively carried out showing good results in many fields. A template matching method is mainly used to recognize parts mounted on PCB(Printed Circuit Board). However, template matching should have multiple templates depending on the shape, orientation and brightness. And it takes long time to perform matching because it searches for the entire image. And there is also a disadvantage that the recognition rate is considerably low. In this paper, we use the Faster-RCNN method for recognizing PCB components as machine learning for classifying several objects in one image. This method performs better than the template matching method, execution time and recognition.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.6-10
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• 2021

Recognition of characters printed or marked on the PCB components from images captured using cameras is an important task in PCB components inspection systems. Previous optical character recognition (OCR) of PCB components typically consists of two stages: character segmentation and classification of each segmented character. However, character segmentation often fails due to corrupted characters, low image contrast, etc. Thus, OCR without character segmentation is desirable and increasingly used via deep neural networks. Typical implementation based on deep neural nets without character segmentation includes convolutional neural network followed by recurrent neural network (RNN). However, one disadvantage of this approach is slow execution due to RNN layers. LPRNet is a segmentation-free character recognition network with excellent accuracy proved in license plate recognition. LPRNet uses a wide convolution instead of RNN, thus enabling fast inference. In this paper, LPRNet was adapted for recognizing characters printed on PCB components with fast execution and high accuracy. Initial training with synthetic images followed by fine-tuning on real text images yielded accurate recognition. This net can be further optimized on Intel CPU using OpenVINO tool kit. The optimized version of the network can be run in real-time faster than even GPU.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.382-395
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• 2021

In this study, character recognition using deep learning is performed among the various defects in the PCB, the purpose of which is to check whether the printed characters are printed correctly on top of components, or the incorrect parts are attached. Generally, character recognition may be perceived as not a difficult problem when considering MNIST, but the printed letters on the PCB component data are difficult to collect, and have very high redundancy. So if a deep learning model is trained with original data without any preprocessing, it can lead to over fitting problems. Therefore, this study aims to reduce the redundancy to the smallest dataset that can represent large amounts of data collected in limited production sites, and to create datasets through data enhancement to train a flexible deep learning model can be used in various production sites. Moreover, ResNet model verifies to determine which combination of datasets is the most effective. This study discusses how to reduce and augment data that is constantly occurring in real PCB production lines, and discusses how to select coresets to learn and apply deep learning models in real sites.