• 전기학회논문지
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• 제60권12호
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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.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 추계학술대회
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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.

• E2M - 전기 전자와 첨단 소재
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• 제7권2호
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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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• 제15권5호
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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.

• 반도체디스플레이기술학회지
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• 제20권3호
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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.

• 대한전자공학회논문지
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• 제24권3호
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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.

• 한국정보과학회논문지:시스템및이론
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• 제30권11호
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• pp.610-620
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• 2003

다양한 그림자는 비젼 기반 차량 검출에서 오류를 발생시키는 주요 원인이다. 본 논문에서는 노면 표시 기반 방법과 배경 빼기 및 에지(BS & Edge) 방법이라는 두 가지 방안을 차량 검출과 그림자 제거를 위해 제안하였다. 노변의 지형 지물들로 인해서 발생하는 그림자의 영향이 크게 증가하는 상황에서의 실험을 통해서 96% 이상의 차량 검출 정확도를 나타냄을 확인하였다. 전술한 두 가지 방법을 기반으로 하여, 차량 추적, 차량 계수, 차종 분류, 그리고 속도 측정을 수행하여 각 차로의 부하를 나타내는 데 사용되는 차량 흐름과 관련된 여러 가지 교통 파라미터를 추출하였다.

• 방송공학회논문지
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• 제5권2호
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• pp.239-246
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• 2000

본 논문에서는 입체 카메라의 자동 초점 제어에 필요한 제어 변수 추출 방법을 제안한다. 일반적으로 카메라로 획득한 입체 영상에서 관심 있는 피사체가 영상의 중심부에 있다고 가정을 한다. 이 경우에 획득된 영상에서 특정한 크기의 중심 영역에 대해서만 2D 웨이블릿 변환을 한다. 변환을 거친 신호 중에서 고역 성분 (HH)에 대한 Ll Norm을 이용하여 필요한 초점제어 변수를 추출한다. 실험결과를 통해 제안된 방식이 기존의 DCT를 이용한 방식에 비해 입체카메라의 자동 초점 제어에 효과적으로 적용될 수 있음을 보인다.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권3호
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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.

• 대한전자공학회논문지SD
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• 제49권4호
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• pp.1-6
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• 2012

본 논문에서는 고주파에서 동작하는 터널링 전계효과 트랜지스터 (TFET)의 소신호 파라미터 추출과 이에 대한 분석을 다루고 있다. 시뮬레이션으로 구현된 TFET의 채널 길이는 50 nm에서 100 nm 사이에서 변화되었다. Conventional planar MOSFET 기반의 quasi-static 모델을 이용하여 TFET의 파라미터 추출이 이루어졌으며 다른 채널 길이를 갖는 TFET에 대한 소신호 파라미터의 값을 게이트 바이어스 변화에 따라서 추출하였다. 추출 결과로부터 effective gate resistance와 transconductance, source-drain conductance, gate capacitance 등 주요 파라미터의 채널 길이 변화에 따른 경향성이 conventional MOSFET과 상당히 다른 것을 확인하였다. 그리고 $f_T$는 MOSFET과 달리 게이트 길이 역수의 값에 정확히 반비례하는 특성을 보였으며 TFET의 고주파 특성 향상을 transconductance의 개선이 아닌 gate capacitance의 감소에 의하여 가능함을 알 수 있었다.