• ETRI Journal
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• v.36 no.4
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• pp.654-661
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• 2014

We develop accurate finite-difference time-domain (FDTD) modeling of polymer bulk heterojunction solar cells containing Ag nanoparticles between the hole-transporting layer and the transparent conducting oxide-coated glass substrate in the wavelength range of 300 nm to 800 nm. The Drude dispersion modeling technique is used to model the frequency dispersion behavior of Ag nanoparticles, the hole-transporting layer, and indium tin oxide. The perfectly matched layer boundary condition is used for the top and bottom regions of the computational domain, and the periodic boundary condition is used for the lateral regions of the same domain. The developed FDTD modeling is employed to investigate the effect of geometrical parameters of Ag nanospheres on electromagnetic fields in devices. Although negative plasmonic effects are observed in the considered device, absorption enhancement can be achieved when favorable geometrical parameters are obtained.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.175-182
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• 2019

We classify topics on fall incidents occurring in construction sites using topic modeling among machine learning techniques and analyze the causes of the accidents according to each topic. In order to apply topic modeling based on latent dirichlet allocation, text data was preprocessed and evaluated with Perplexity score to improve the reliability of the model. The most common falling accidents happened to the daily workers belonging to small construction site. Most of the causes were not operated properly due to lack of safety equipment, inadequacy of arrangement and wearing, and low performance of safety equipment. In order to prevent and reduce the falling accidents, it is important to educate the daily workers of small construction site, arrange the workplace, and check the wearing of personal safety equipment and device.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.645-654
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• 2019

The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.06a
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• pp.485-485
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• 2009

Double side polishing process has been used for various industrial applications, such as polishing of semiconductor substrates and flat panel display glasses. In wafer manufacturing, double side polishing process is applied to improve wafer flatness and to minimize particle generation from wafers in device manufacturing processes, which is recognized as one of the most important processes. Whereas the kinematical modeling and analysis results of single side polishing, extensively used for chemical-mechanical polishing (CMP) in device manufacturing, are well investigated, the studies in conjunction with double side polishing are barely carried out, due to the complication of polishing system and the uncertainty of wafer motion in the carrier. This paper suggests the derivation of kinematical model with consideration of carrier and wafer motion in double side polishing, and then presents the effect of kinematical parameters on material removal amount and its non-uniformity. The kinematical analysis results help to understand the double side polishing process and to control the polishing results.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.17-21
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• 2015

In this paper, we introduce satellite communication for new wireless disaster network to be built on the basis of amateur radio HR (HAM Radio) as a wireless package system, and channel environments of a D2D terminal that tries to connect and communicate with the wireless disaster network. In this disaster network, we propose a LOS component ratio based adaptive channel modeling approach to accurately estimate a variety of channels whose the D2D terminal could have and smoothly transfer to the level of multimedia data based on the Okumura-Hata channel model. As a result of computer simulation, performance of the proposed method was compared with the that of Okumura-Hata model of open area and urban area model and we were confirmed that there is a gain of BER performance from the results of the computer simulation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.7 s.337
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• pp.5-12
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• 2005

The device performance of nano-scale center-channel (CC) double-gate (DG) MOSFET structure was investigated by numerically solving coupled Schr$\"{o}$dinger-Poisson and current continuity equations in a self-consistent manner. The CC operation and corresponding enhancement of current drive and transconductance of CC-NMOS are confirmed by comparing with the results of DG-NMOS which are performed under the condition of 10-80 nm gate length. Device optimization was theoretically performed in order to minimize the short-channel effects in terms of subthreshold swing, threshold voltage roll-off, and drain-induced barrier lowering. The simulation results indicate that DG-MOSFET structure including CC-NMOS is a promising candidates and quantum-mechanical modeling and simulation calculating the coupled Schr$\"{o}$dinger-Poisson and current continuity equations self-consistently are necessary for the application to sub-40 nm MOSFET technology.

• Journal of KIISE
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• v.43 no.4
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• pp.460-469
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• 2016

Internet of Things (IoT) is an environment where various devices are connected to each other via a wired/wireless network and where the devices gather, process, exchange, and share information. Some of the most important types of IoT devices are mobile IoT devices such as smartphones. These devices provide various high-performance services to users but cannot be supplied with power all the time; therefore, power management appropriate to a given IoT environment is necessary. Power management of mobile IoT devices involves complex relationships between various entities such as application processors (APs), HW modules inside/outside AP, Operating System (OS), platforms, and applications; a method is therefore needed to systematically analyze and manage these relationships. In addition, variabilities related to power management such as various policies, operational environments, and algorithms need to be analyzed and applied to power management development. In this paper, engineering principles and a method based on them are presented in order to address these challenges and support systematic development of IoT device power management. Power management of connected helmet systems was used to validate the feasibility of the proposed method.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.278-281
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• 2011

This system can predict the maximum output about all illumination levels so that the PV system designer can design the system having the best efficiency. For the output prediction exact about the solar cell, that is the device the basis most in the PV system, the basis has to be in order to try this way. The solution based on Lambert W-function are presented to express the transcendental current-voltage characteristic containing parasitic power consuming parameters like series and shunt resistances. A simple and efficient method for the extraction of a single current-voltage (I-V) curve under the constant illumination level is proposed. With the help of the Lambert W function, the explicit analytic expression for I is obtained. And the explicit analytic expression for V is obtained. This analytic expression is directly used to fit the experimental data and extract the device parameters. The I-V curve of the solar cell was expressed through the modeling using Lambert W-function and the numerical formula where there is the difficulty could be logarithmically expressed This method expresses with the I-V curve through the modeling using Lambert W-function which adds other loss ingredients to the equation2 as to the research afterward. And the solar cell goes as small and this I-V curve can predict the power penalty in the system unit.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.875-880
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• 2007

Scientific challenges in the field of MR(magnetorheological) fluids and devices consist in the development of MR devices, the mathematical modeling and simulation of MR devices, and the development of (optimal) control algorithm for MR device systems. To take a maximum advantage of MR fluids in control applications a reliable mathematical model, which predicts their nonlinear characteristics, is needed. A inverse model of the MR device is required to calculate current(or voltage) input of MR damper, which generates required damping force. In this paper, we implemented test a bench for shear mode rotary MR damper and laboratory tests were performed to study the characteristics of the prototype shear-mode rotary MR damper. The direct identification and inverse dynamics modeling for shear mode rotary MR dampers using multi-layer neural networks are studied.