• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.1-6
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• 2020

In this paper, a method for improving the defect classification performance in steel plate surface has been studied, based on DWT(discrete wavelet transform) and SVM(support vector machine). Surface images of the steel plate have low contrast, uneven, and featureless, so that the contrast between defect and defect-free regions is not discriminated. These characteristics make it difficult to extract the feature of the surface defect image. In order to improve the characteristics of these images, a synthetic images based on discrete wavelet transform are modeled. Using the synthetic images, edge-based features are extracted and also geometrical features are computed. SVM was configured in order to classify defect images using extracted features. As results of the experiment, the support vector machine based classifier showed good classification performance of 94.3%. The proposed classifier is expected to contribute to the key element of inspection process in smart factory.

• Journal of the Semiconductor & Display Technology
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• v.13 no.2
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• pp.23-28
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• 2014

For the reconstruction of three-dimensional (3D) shape of microscopic objects through shape from focus (SFF) methods, usually a single focus measure operator is employed. However, it is difficult to compute accurate depth map using a single focus measure due to different textures, light conditions and arbitrary object surfaces. Moreover, real images with diverse types of illuminations and contrasts lead to the erroneous depth map estimation through a single focus measure. In order to get better focus measurements and depth map, we have combined focus measure operators by using genetic algorithm. The resultant focus measure is obtained by weighted sum of the output of various focus measure operators. Optimal weights are obtained using genetic algorithm. Finally, depth map is obtained from the refined focus volume. The performance of the developed method is then evaluated by using both the synthetic and real world image sequences. The experimental results show that the proposed method is more effective in computing accurate depth maps as compared to the existing SFF methods.

• Journal of the Semiconductor & Display Technology
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• v.15 no.4
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• pp.1-9
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• 2016

The warpage on mobile phone camera packages occurs due to the CTE(Coefficient of Thermal Expansion) mismatch between a thin silicon die and a substrate. The warpage in the optical instruments such as camera module has an effect on the field curvature, which is one of the factors degrading the optical performance and the product yield. In this paper, we studied the effect of die bonding epoxy on the package and optical performance of mobile phone camera packages. We calculated the warpages of camera module packages by using a finite element analysis, and their shapes were in good agreement showing parabolic curvature. We also measured the warpages and through-focus MTF of camera module specimens with experiments. The warpage was improved on an epoxy with low elastic modulus at both finite element analysis and experiment results, and the MTF performance increased accordingly. The results show that die bonding epoxy affects the warpage generated on the image sensor during the packaging process, and this warpage eventually affects the optical performance associated with the field curvature.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• Journal of the Semiconductor & Display Technology
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• v.13 no.1
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• pp.27-33
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• 2014

Video from unmanned aerial vehicle (UAV) is influenced by natural environments due to the light-weight UAV, specifically by winds. Thus UAV's shaking movements make the video shaking. Objective of this paper is making a stabilized video by removing shakiness of video acquired by UAV. Stabilizer estimates camera's motion from calculation of optical flow between two successive frames. Estimated camera's movements have intended movements as well as unintended movements of shaking. Unintended movements are eliminated by smoothing process. Experimental results showed that our proposed method performs almost as good as the other off-line based stabilizer. However estimation of camera's movements, i.e., calculation of optical flow, becomes a bottleneck to the real-time stabilization. To solve this problem, we make parallel stabilizer making average 30 frames per second of stabilized video. Our proposed method can be used for the video acquired by UAV and also for the shaking video from non-professional users. The proposed method can also be used in any other fields which require object tracking, or accurate image analysis/representation.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.18-23
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• 2016

QFN (Quad Flat No-leads Package) is one of the SMD (Surface Mount Device). Since there is no lead in QFN, there are many defects on solder. Therefore, we propose an efficient mechanism for QFN solder defect detection at this paper. For this, we employ Convolutional Neural Network (CNN) of the Machine Learning algorithm. QFN solder's color multi-layer images are used to train CNN. Since these images are 3-channel color images, they have a problem with applying to CNN. To solve this problem, we used each 1-channel grayscale image (Red, Green, Blue) that was separated from 3-channel color images. We were able to detect QFN solder defects by using this CNN. In this paper, it is shown that the CNN is superior to the conventional multi-layer neural networks in detecting QFN solder defects. Later, further research is needed to detect other QFN.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.129-137
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• 2000

In this paper, a new method of noncontact measurement has been developed for a 3 dimensional topography in semiconductor wafer, implementing a new optical probe based on the precision defocus measurement. The developed technique consists of the new optical probe, precision stages, and the measurement/control system. The basic principle of the technique is to use the reflected slit beam from the specimen surface, and to measure the deviation of the specimen surface. The defocusing distance can be measured by the reflected slit beam, where the defocused image is measured by the proposed optical probe, giving very high resolution. The distance measuring formula has been proposed for the developed probe, using the laws of geometric optics. The precision calibration technique has been applied, giving about 10 nanometer resolution and 72 nanometer of four sigma uncertainty. In order to quantitize the micro pattern in the specimen surface, some efficient analysis algorithms have been developed to analyse the 3D topography pattern and some parameters of the surface. The developed system has been successfully applied to measure the wafer surface, demonstrating the line scanning feature and excellent 3 dimensional measurement capability.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.1-5
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• 2018

Atomic force microscopy (AFM) simulation for Si (001) surface defects was conducted by using density functional theory (DFT). Three major defects on the Si (001) surface are difficult to analyze due to external noises that are always present in the images obtained by AFM. Noise-free surface defects obtained by simulation can help identify the real surface defects on AFM images. The surface defects were first optimized by using a DFT code. The AFM tip was designed by using five carbon atoms and positioned on the surface to calculate the system's energy. Forces between tip and surface were calculated from the energy data and converted into an AFM image. The simulated AFM images are noise-free and, therefore, can help evaluate the real surface defects present on the measured AFM images.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.58-62
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• 2019

Diffractive Optical Elements(DOEs) diffracts incident light using the diffraction phenomenon of light to generate a desired diffraction image. In recent years, the use of diffraction optics, which can replace existing refractive optical elements with flat plates, has been increased by implementing various optical functions that could not be implemented in refractive optical devices and by becoming miniaturized and compacted optical elements. Direct laser lithography is typically used to effectively fabrication such a diffractive optical element in a large area with a low process cost. In this study, the process conditions for fabricating patterns of diffractive optical elements in various shapes were found using direct laser lithographic system, and optical performance evaluation was performed through fabrication.