• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1149-1155
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• 2012

In this paper, we propose cause diagnosis method of semiconductor defects from semiconductor industrial images. Our method constructs feature database (DB) of defect images. Then, defect and input images are subdivided by uniform block. And the block similarity is measured using histogram kai-square distance after color histogram calculation. Then, searched blocks in each image are merged into connected objects using clustering. Finally, the most similar defect image from feature DB is searched with the defect cause by measuring cluster similarity based on features of each cluster. Our method was validated by calculating the search accuracy of n output images having high similarity. With n = 1, 2, 3, the search accuracy was measured to be 100% regardless of defect categories. Our method could be used for the industrial applications.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.263-279
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• 2022

This paper presents an automatic inspection of defects in semiconductor images. We devise a statistical method to find defects on homogeneous background from the observation that it has a log-normal distribution. If computer aided design (CAD) data is available, we use it to construct a signed distance function (SDF) and change the pixel values so that the average of pixel values along the level curve of the SDF is zero, so that the image has a homogeneous background. In the absence of CAD data, we devise a hybrid method consisting of a model-based algorithm and two neural networks. The model-based algorithm uses the first right singular vector to determine whether the image has a linear or complex structure. For an image with a linear structure, we remove the structure using the rank 1 approximation so that it has a homogeneous background. An image with a complex structure is inspected by two neural networks. We provide results of numerical experiments for the proposed methods.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2735-2738
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• 2011

To obtain the Si solar cells, the Si-wafers were textured by using the IPA+DI water mixed solution with KOH acids during the various 1~40 minutes at the temperature with $80^{\circ}C$, respectively. The samples were analyzed by the scanning electron microscopy for the surface images and the solar simulation for I-V measurement system. It was researched the correlation between the efficiency of solar cells and the effect of texturing. From the results of the surface images obtained by SEM, the efficiency was increased at the sample textured uniformly, and the efficiency of over etched-samples decreased.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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• pp.87-91
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• 2010

Charged micro-particles are widely used as the key components for many electrical applications such as an e-paper, a touch panel, a printer toner and an electronic ink. Among them, the e-paper is an emerging reflective type display using the charged particles that has the advantages of the extremely low power consumption and sunlight readability. To create images on the e-paper, we confine black positively-charged and white negatively-charged particles between bottom and top electrodes and selectively apply the electric field. When the Coulomb force by an applied electric field is greater than the adhesion force between the charged particle and the electrode, the particles' transition happens resulting in the change of color between black and white. Therefore, the adhesion force is a very important factor for designing and estimating e-paper's operation. In this study, we constructed a basic model for particle's transition and an adhesion force equation describing particle's transition with three different forces: electrostatic image force, Van der Waals force and gravitational force. The simulation results showed that the gravitational force is negligible for the interesting range for the charge and the radius, and the adhesion force can be strongly dependent on the particle's charge and radius.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.446-452
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• 2020

As the use of children's school buses increases, accidents caused by the negligence of school bus drivers and ride carers have also increased significantly. To prevent such accidents, the government is coming up with various policies. We propose an accident prevention and safety management system for children's school buses. Through this system, bus drivers can easily check whether each child is seated and whether the seat belt is used, so it is possible to quickly respond to children's conditions while driving. With the ability to recognize faces by analyzing camera images, children can use a seat belt that is automatically adjusted to their height. It is therefore possible to prevent secondary injuries that may occur in the event of a traffic accident. In addition, a sleeping child-check system is provided to confirm that all children get off the bus, and a text service is provided to inform parents of their children's locations in real time. Based on Raspberry Pi, the system is implemented with cameras, pressure sensors, motors, Bluetooth modules, and so on. This proposed system was attached to a bus model to confirm that the series of functions work correctly.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.960-967
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• 2013

Shape recognition of solder ball bumps in a BGA (Ball Grid Array) is an important issue in flip chip bonding technology. In particular, the semiconductor industry has required faster and more accurate inspection of micron-size solder bumps in flip chip bonding as the density of balls has increased dramatically. The difficulty of this issue comes from specular reflection on the metal ball. Shape recognition of a metal ball is a very realproblem for computer vision systems. Specular reflection of the metal ball appears, disappears, or changes its image abruptly due to tiny movementson behalf of the viewer. This paper presents a practical shape recognition method for three dimensional (3-D) inspection of a BGA using a 5-step ring illumination device. When the ring light illuminates the balls, distinctive specularity images of the balls, which are referred to as "iso-slope contours" in this paper, are shown. By using a mathematical reflectance model, we can drive the 3-D shape information of the ball in aquantitative manner. The experimental results show the usefulness of the method for industrial application in terms of time and accuracy.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.4
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• pp.258-265
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• 2016

Digital printing has been used in various industrial areas, including semiconductor manufacturing and textile printing. However, implications on ceramic textile have not been well established so far. Printing high-viscosity materials requires an understanding of their behavior. An inorganic high viscous material with a viscosity range of 20-30 cps is analyzed using a viscometer and through X-ray diffraction. In this study, a digital printer is designed and assembled using a high-viscosity material with software for PC control, resulting in reduced processing at a fast area velocity of $20m^2/hr$. The present study demonstrated that the printer is capable of controlling the shape of the drop mass to smear ink smoothly onto the ceramic surface under an economic budget. In addition, to avoid any difficulty in color management, the ceramic printer is equipped with an independent color management system designed to cope with images on a highly viscous material.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.