• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.3
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• pp.146-151
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• 2018

In this study, we compared the threshold-voltage extraction methods of accumulation-type JLDG (junctionless double-gate) MOSFETs (metal-oxide semiconductor field-effect transistors). Threshold voltage is the most basic element of transistor design; therefore, accurate threshold-voltage extraction is the most important factor in integrated-circuit design. For this purpose, analytical potential distributions were obtained and diffusion-drift current equations for these potential distributions were used. There are the ${\phi}_{min}$ method, based on the physical concept; the linear extrapolation method; and the second and third derivative method from the $I_d-V_g$ relation. We observed that the threshold-voltages extracted using the maximum value of TD (third derivatives) and the ${\phi}_{min}$ method were the most reasonable in JLDG MOSFETs. In the case of 20 nm channel length or more, similar results were obtained for other methods, except for the linear extrapolation method. However, when the channel length is below 20 nm, only the ${\phi}_{min}$ method and the TD method reflected the short-channel effect.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.2
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• pp.140-146
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• 2015

We have studied an efficient threshold level for desired target detection in radar system in the paper. A desired target searching detection method detects desired target according to changing for false alarm probability. This time, false alarm probability is close relation to threshold level. Low threshold level can improve detection for desired target, but detect noise signal. Therefor, This method is not good one. In this paper, we propose efficient threshold level method in order to estimation for desired target. Through simulation, we are analysis and performance to compare general method with proposal method. We show that proposed method is more good proof than general method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.57-65
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• 1998

This paper addresses the data classification using the induction of decision trees. A weakness of other techniques of induction of decision trees is that decision trees are too large because they construct decision trees until leaf nodes have a single class. Our study include both overcoming this weakness and constructing decision trees which is small and accurate. First, we construct the decision trees using classification threshold and exception threshold in construction stage. Next, we present two stage pruning method using classification threshold and reduced error pruning in pruning stage. Empirical results show that our method obtain the decision trees which is accurate and small.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.33-40
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• 2005

The estimating function method was proposed by Godambe(1985) for parameter estimation under unknown distributions for errors in the models. Threshold Autoregressive Heteroscedastic (Threshold-ARCH) models have been developed by Zakoian(1994) and Li and Li(1996) for explaining the asymmetric properties in the financial time series data. In this paper, we apply the estimating function method to the Threshold-ARCH model and show that the proposed estimators perform better than the MLE under the heavy-tailed distributions.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.1
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• pp.1-7
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• 2012

As the standard lighting required in the threshold zone of a road tunnel is determined by the enough contrast. Lighting design, therefore, must be determined by the calculation method of threshold luminance based on the adaptation luminance of the driver approaching the tunnel. The veiling luminance and the luminance in the access zone were measured at different time and in different weather using the veiling luminance method, a kind of perceived contrast methods, and the L20 method when the range of vision was 20 degrees. On the basis of the measured data each threshold luminance was calculated and its results were analyzed.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.6
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• pp.213-224
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• 2003

The proposed algorithm can always set up the optimal threshold value regardless of channel characteristics using an adaptive threshold establishment method that determines the threshold level according to channel noise power, and then it uses the specially designed training symbols that can make the algorithm's estimation performance be less sensitive to power delay profile variation in a multipath channel. In result, the estimation performance of the proposed technique is less affected by channel characteristic variation.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.167-175
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• 2007

Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.

• Journal of the Semiconductor & Display Technology
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• v.19 no.1
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• pp.61-66
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• 2020

Threshold voltage shift occurring during operation is implemented in a SPICE simulation tool. Among the shift models the stretched-exponential function model, which is frequently observed from both single-crystal silicon and thin-film transistors regardless of the nature of causes, is selected, adapted to transient simulation, and added to BSIM4 developed by BSIM Research Group at the University of California, Berkeley. The adaptation method used in this research is to select degradation and recovery models based on the comparison between the gate and threshold voltages. The threshold voltage shift is extracted from SPICE transient simulation and shows the stretched-exponential time dependence for both degradation and recovery situations. The implementation method developed in this research is not limited to the stretched-exponential function model and BSIM model. The proposed method enables to perform transient simulation with threshold voltage shift in situ and will help to verify the reliability of a circuit.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.7 no.2
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• pp.55-58
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• 1982

An useful method for evaluating the threshold of the optimum detector can be used if sufficient statistics exists. This was done by giving examples. The design of the suboptimum detector can be carried out by finding the threshold of the appropriate test statistics. The results are conformed with the existing theory and the method given above is applicable in extensive area of designing the detectors.

• Journal of the Korean Society of Safety
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• v.15 no.3
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• pp.1-6
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• 2000

In fatigue crack growth test, it is important not only to analyze characteristics of fatigue crack growth but also to determine the threshold stress intensity factor, ${\Delta}K_{th}$. which is the threshold value of fatigue crack growth. Linear regression analysis using fatigue test data near the threshold is suggested to determine the ${\Delta}K_{th}$ in the standard test method but the ${\Delta}K_{th}$ can be affected by a fitting method. And there are some limitations on the linear regression analysis in the case of small number of test data near the threshold. The objective of this study is to investigate differences of the ${\Delta}K_{th}$ due to regression analysis method and to evaluate the relative error range of the ${\Delta}K_{th}$ in same fatigue crack growth test data.