• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.2
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• pp.171-175
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• 2014

In this study, we suggest a method to measure the thickness of thin films nondestructively using the dispersion characteristics of a surface acoustic wave propagating along the thin film surface. To measure the thickness of thin films, we deposited thin films with different thicknesses on a Si (100) wafer substrate by controlling the deposit time using the E-beam evaporation method. The thickness of the thin films was measured using a scanning electron microscope. Subsequently, the surface wave velocity of the thin films with different thicknesses was measured using the V(z) curve method of scanning acoustic microscopy. The correlation between the measured thickness and surface acoustic wave velocity was verified. The wave velocity of the film decreased as the film thickness increased. Therefore, thin film thickness can be determined by measuring the dispersion characteristics of the surface acoustic wave velocity.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.205-206
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• 2009

• Journal of the Korean Society for Nondestructive Testing
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• v.9 no.2
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• pp.8-17
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• 1989

Reconstruction of the radial two-dimensional of fission products using projections obtained by nondestructive transversal direction gamma scanning was evaluated, and then compared with the destructive sectional gamma scanning results. Although the nondestructive method has a little drawback compared to the destructive method in the accuracy, the former seemed to be sufficiently applicable.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.105-110
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• 1999

This paper presents a local obstacle avoidance method for indoor mobile robots using Lane method and velocity Space Command approach. The method locates local obstacles using the information form multi-sensors, such that ultrasonic sensor array and laser scanning sensor. The method uses lane method to determine optimum collision-free heading direction of a robot. Also, it deals with the robot motion dynamics problem to reduce some vibration and guarantee fast movement as well. It yields translational and rotational velocities required to avoid the detected obstacles and to keep the robot heading direction toward goal location as close as possible. For experimental verification of the method, a mobile robot driven by two AC servo motors, equipped with 24 ultrasonic sensor array and laser scanning sensor navigates using the method through a corridor cluttered with obstacle.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.108-113
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• 2022

The deep-learning-based measurement method with the through-focus scanning optical microscopy (TSOM) estimated the size of the object using the classification. However, the measurement performance of the method depends on the number of subdivided classes, and it is practically difficult to prepare data at regular intervals for training each class. We propose an approach to measure the size of an object in the TSOM image using the deep-learning regression model instead of using classification. We attempted our proposed method to estimate the top critical dimension (TCD) of through silicon via (TSV) holes with 2461 TSOM images and the results were compared with the existing method. As a result of our experiment, the average measurement error of our method was within 30 nm (1σ) which is 1/13.5 of the sampling distance of the applied microscope. Measurement errors decreased by 31% compared to the classification result. This result proves that the proposed method is more effective and practical than the classification method.

• Journal of KIISE:Information Networking
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• v.35 no.6
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• pp.474-481
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• 2008

Scanning worm increases network traffic load and result in severe network congestion because it is a self-replicating worm and send copies of itself to a number of hosts through the Internet. So an early detection system which can automatically detect scanning worms is needed to protect network from those attacks. Although many studies are conducted to detect scanning worms, most of them are focusing on the method using packet header information. The method using packet header information has long detection delay since it must examine the header information of all packets entering or leaving the network. Therefore we propose an algorithm to detect scanning worms using network traffic characteristics such as variance of traffic volume, differentiated traffic volume, mean of differentiated traffic volume, and product of mean traffic volume and mean of differentiated traffic volume. We verified the proposed algorithm by analyzing the normal traffic captured in the real network and the worm traffic generated by simulator. The proposed algorithm can detect CodeRed and Slammer which are not detected by existing algorithm. In addition, all worms were detected in early stage: Slammer was detected in 4 seconds and CodeRed and Witty were detected in 11 seconds.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1660-1665
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• 2022

Recently, many industries are using three dimensional scanning technology. As the performance of the 3D scanner gradually improves, a sampling step to reduce a point data or a remove step to remove a part determined to be noise are generally performed in post processing. However, total point data by long time scanning cannot be processed at once in spite of performing such those additional processes. In general, a method using a multi threaded environment is widely used, but as the scanning process work time increases, the processing performance gradually decreases due to various environmental conditions and accumulated operations. This paper proposes a method to initially remove point data judged to be unnecessary by calculating accumulated fast point feature histogram values from coming point data of the 3D scanner in real time. The entire 3D scanning process can be reduced using this approach.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.137-140
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• 1996

Finding line segments in an intensity image has been one of the most fundamental issues in computer vision. In complex scenes, it is hard to detect the locations of point features. Line features are more robust in providing greater positional accuracy. In this paper we present a robust "line features extraction" algorithm which extracts line feature in a single pass without using any assumptions and constraints. Our algorithm consists of five steps: (1) edge scanning, (2) edge normalization, (3) line-blob extraction, (4) line-feature computation, and (5) line linking. By using edge scanning, the computational complexity due to too many edge pixels is drastically reduced. Edge normalization improves the local quantization error induced from the gradient space partitioning and minimizes perturbations on edge orientation. We also analyze the effects of edge processing, and the least squares-based method and the principal axis-based method on the computation of line orientation. We show its efficiency with some real images.al images.

• Applied Microscopy
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• v.45 no.4
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• pp.214-217
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• 2015

This study introduces metal coating as an effective sample preparation method to remove charge-up caused by the shadow effect during field emission scanning electron microscope (FE-SEM) analysis of dynamic structured samples. During a FE-SEM analysis, charge-up occurs when the primary electrons (input electrons) that scan the specimens are not equal to the output electrons (secondary electrons, backscattered electrons, auger electrons, etc.) generated from the specimens. To remove charge-up, a metal layer of Pt, Au or Pd is applied on the surface of the sample. However, in some cases, charge-up still occurs due to the shadow effect. This study developed a coating method that effectively removes charge-up. By creating a converted sample stage capable of simultaneous tilt and rotation, the shadow effect was successfully removed, and image data without charge-up were obtained.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.667-673
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• 2005

This paper shows a method for design of the nano-positioning planar scanner used in the scanning probe microscope. The planar scanner is composed of flexure guides, piezoelectric actuators and feedback sensors. In the design of flexure guides, the Castigliano's theorem was used to find the stiffness of the guide. The motion amplifying mechanism was used in the piezoelectric actuator to achieve a large travel range. We found theoretically the travel range of the total system and verified using the commercial FEM(finite element method) program. The maximum travel range of the planar scanner is above than 140 $\mu$m. The 3 axis positioning capability was verified by the mode analysis using the FEM program.