• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2281-2285
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• 2011

An algorithm for extracting SPICE MOS level 2 model parameters for the high voltage MOSFET DC model is proposed. The optimization method for analyzing the nonlinear data of the current-voltage curve using the Gauss-Newton algorithm is proposed and the pre-process step for calculating the threshold voltage and the mobility is proposed. The drain current obtained from the proposed method shows the maximum relative error of 5.6% compared with the drain current of 2-dimensional device simulation for the high voltage MOSFET.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.25-28
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• 1990

We tested the ability of two-dimensional echocardiograms of complete heart cycle in closed-chest human to discriminate between normal and infarcted myocardium using fixed window, Inter- and Intra-frame analysis. The results show that statistical parameter, MEAN, second order gray level statistics parameter, ASM and proposed parameter, HGE, I.T, can quantitatively distinguish between normal and Infarcted regions. The manner in which these parameters vary over the cardiac cycle is also a good indicator of the state of myocardium. The infarcted areas yield regions of higher Intensity throughout the cardiac cycle. Whereas, normal tissue demonstrates greater variability throughout the cardiac cycle.

• Electrical & Electronic Materials
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• v.7 no.2
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• pp.107-116
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• 1994

In the manufacturing of VLSI circuits, variations of device characteristics due to the slight differences in process parameters drastically aggravate the performances of fabricated devices. Therefore, it is very important to establish optimal process conditions in order to minimize deviations of device characteristics. In this paper, we used one-dimensional process simulator, SUPREM-II, and two dimensional device simulator, MINIMOS 4.0 in order to extract optimal process parameter which can minimize changes of the device characteristics caused by process parameter variation in the case of short channel nMOSFET and pMOSFET device. From this simulation, we have derived the dependence relations between process parameters and device characteristics. Here, we have suggested a method to extract process parameters from design trend curve(DTC) obtained by these dependence relations. And we have discussed short channel effects and device limitations by scaling down MOSFET dimensions.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.5
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• pp.485-489
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• 2015

An accurate large-signal BSIM4 macro model including new empirical bias-dependent equations of the drain-source capacitance and channel resistance constructed from bias-dependent data extracted from S-parameters of RF MOSFETs is developed to reduce $S_{22}$-parameter error of a conventional BSIM4 model. Its accuracy is validated by finding the much better agreement up to 40 GHz between the measured and modeled $S_{22}$-parameter than the conventional one in the wide bias range.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.131-135
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• 2021

Numbers of studies related to optimization of design of organic light emitting diodes(OLED) through machine learning are increasing. We propose the generative method of the image to assess the performance of the device combining with machine learning technique. Principle parameter regarding dark spot growth mechanism of the OLED can be the key factor to determine the long-time performance. Captured images from actual device and randomly generated images at specific time and initial pinhole state are fed into the deep neural network system. The simulation reinforced by the machine learning technique can predict the device parameters accurately and faster. Similarly, the inverse design using multiple layer perceptron(MLP) system can infer the initial degradation factors at manufacturing with given device parameter to feedback the design of manufacturing process.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.3
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• pp.381-386
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• 1987

By the speech production model, the first positive peak in a pitch interval of the voiced speech is mainly affected by the glottis and the first formant component, known as a typical energy source of the voiced speech. From these characteristics, the energy parameter can be replaced by the area of the area of the positve peak in a pitch interval, which parameter is generally used for classification of speech signals. In this method, the changed energy parameter is independent of window length applied for analysis, and the pitch can be extracted smultaneously. Furthermore, the energy can be extracted in the pitch period unit.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.11
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• pp.610-620
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• 2003

Various shadows are one of main factors that cause errors in vision based vehicle detection. In this paper, two simple methods, land mark based method and BS & Edge method, are proposed for vehicle detection and shadow rejection. In the experiments, the accuracy of vehicle detection is higher than 96%, during which the shadows arisen from roadside buildings grew considerably. Based on these two methods, vehicle counting, tracking, classification, and speed estimation are achieved so that real-time traffic parameters concerning traffic flow can be extracted to describe the load of each lane.

• Journal of Broadcast Engineering
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• v.5 no.2
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• pp.239-246
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• 2000

An efficient control parameter extraction scheme required for auto-focusing control of a stereo camera is proposed. Without loss of generality, it is assumed that an interesting object exists in the center of a captured image by a stereo camera. In such a case. we apply a 2-dimensional wavelet transform to the center area with specific image size in the captured image. Next, we extract required focus control parameters using an Ll-norm for doubly high-pass filtered components. Experimental results show that the proposed scheme is effectively applicable to the auto-focusing for a stereo camera compared to the conventional control scheme using discrete cosine transform (DCT).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.3
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• pp.122-128
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• 2002

In this paper, a robust pattern classification of breath sounds for the diagnosis of pulmonary dysfunction in noise environment is proposed. The feature parameter extraction method by highpass lifter algorithm and PM(projection measure) algorithm are used. 17 different groups of breath sounds are experimentally classified and investigated. The classification has been performed by 6 different types of combinations with proposed methods to evaluate the performances, such as ARC with EDM and LCC with EDM, WLCC with EDM, ARC with PM, LCC with PM, WLCC with PM. Furthermore, all feature parameters are extracted to 80th orders by 5th orders step, and all experiments are evaluated in increasing noise environments by degrees SNR 24dB to 0dB. As a results, WLCC which is derived from highpass lifter algorithm, is selected for the feature parameter extraction method. Pm is more robust than EDM in noisy environments to test and compare experimental results. WLCC with PM method(WLCC/PM) has a better performance in an increasing noise environment for diagnosis of pulmonary dysfunction.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.4
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• pp.1-6
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• 2012

This paper presents the extraction and analysis of small-signal parameters of tunneling field-effect transistors (TFETs) by using TCAD device simulation. The channel lengths ($L_G$) of the simulated devices varies from 50 nm to 100 nm. The parameter extraction for TFETs have been performed by quasi-static small-signal model of conventional MOSFETs. The small-signal parameters of TFETs with different channel lengths were extracted according to gate bias voltage. The $L_G$-dependency of the effective gate resistance, transconductance, source-drain conductance, and gate capacitance are different with those of conventional MOSFET. The $f_T$ of TFETs is inverely proportional not to $L_G{^2}$ but to $L_G$.