• JSTS:Journal of Semiconductor Technology and Science
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• v.10 no.2
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• pp.100-106
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• 2010

The impact of surface quantization on device parameters of a Si metal oxide semiconductor (MOS) capacitor has been analyzed in the present work. Variation of conduction band bending, position of discrete energy states, variation of surface potential, and the variation of inversion carrier concentration at charge centroid have been analyzed for different gate voltages, substrate doping concentrations and oxide thicknesses. Oxide thickness calculated from the experimental C-V data of a MOS capacitor is different from the actual oxide thickness, since such data include the effect of surface quantization. A correction factor has been developed considering the effect of charge centroid in presence of surface quantization at strong inversion and it has been observed that the correction due to surface quantization is crucial for highly doped substrate with thinner gate oxide.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1897-1902
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• 2008

In computer vision analysis of detecting concrete slab surface cracks, there are many difficulties to overcome. Target images often have defamations due to the light condition and other external environment. Another difficulties in detecting concrete crack image is that there is no clear distinction in intensity between the crack and the surface since the surface is often irregular. In this paper, we apply ART2 based quantization in order to classify target concrete slab surface images into several areas with respect to the light intensity. From those quantized areas, we investigate the distribution of real cracks and noises. Then, we extract candidate crack areas after applying noise removal process to areas which have be th oracle and noises. Finally, crack areas are recognized by using morphological features of cracks from such candidate areas. In experiment with real world concrete slab structure images, our algorithm has advantage in recognizing accuracy of cracks to other algorithms especially in relatively brighter areas of concrete surface.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.49-56
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• 1998

Super precision working technique and machine tool have been continually developed thanks to advanced electronic field. To obtain good result. it is necessary to investigate surface in grinding with $mu extrm{m}$ level. There were quite many researches to satisfy these demands by using non-contact methods through the computer vision. In this study, the texture of working surface was analyzed. co-occurrence matrices was obtained from the surface roughness. Texture parameter was obtained using position operator composed of $ heta$, d according to variation of angle direction and distance. As a result, it was found that surface texture was more affected by direction($\theta$) than distance(d).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.316-322
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• 1998

Super precision working technique and machine tool have been developing continually thanks to advanced electronic field. To obtain good result. it is necessary to investigate surface state in grinding with ${\mu}{\textrm}{m}$ level. There were so many researches to satisfy these demands using non-contact methods through the computer vision. In this study, the texture of working surface was analyzed. cooccurrence matrice was obtained from the surface roughness. Texture parameter was obtained by means of position operator compose of $\theta$. d according to variation of angle direction and distance. As a result, it was found that surface texture was more effected by direction ($\theta$) then distance(d).

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.75-82
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• 2000

This paper presents a method of detecting surface flaw of cold-rolled steel plate using image processing technique and a neural network classifier. The amount of steel plate surface image data is reduced by the wavelet transform. Features are extracted from the co-occurence matrix of the partial image corresponding to the low-frequency region, and a MLP neural network classifies into predetermined surface flaw categories. Simulations show the neural network classifier outperforms conventional vector quantization method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.144-150
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• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1254-1260
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• 2000

There have been many developments in super precision working technique and working method up to, now. But, it is very difficult to evaluate working surface accurately without the technicians experience and judgment. Surface roughness tester using stylus was used to measure surface condition generally But this method is not so desirable because of damage on test piece caused by contact between the workpiece and the stylus sensor. As a result, non-contact method was known as a good way to carry, out this process without damage. However, this is a difficult one among the various measuring methods. So we are tying to suggest a new method using texture analysis through image processing to get a surface information in worked test piece. Co-occurrence matrix using difference of gray levels between a pixel and its neighboring one was used to study behavior of surface roughness and to J acquire data for analysis. Standard specimen was adapted to verify this research. We suggest texture information method in order to evaluate surface state for the best measurement system.

• JSTS:Journal of Semiconductor Technology and Science
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• v.4 no.3
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• pp.228-239
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• 2004

In this paper, an analytical model accounting for the quantum effects in MOSFETs has been developed to study the behaviour of $high-{\kappa}$ dielectrics and to calculate the threshold voltage of the device considering two dielectrics gate stack. The effect of variation in gate stack thickness and permittivity on surface potential, inversion layer charge density, threshold voltage, and $I_D-V_D$ characteristics have also been studied. This work aims at presenting a relation between the physical gate dielectric thickness, dielectric constant and substrate doping concentration to achieve targeted threshold voltage, together with minimizing the effect of gate tunneling current. The results so obtained are compared with the available simulated data and the other models available in the literature and show good agreement.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.513-518
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• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason. so many studies were carried out through various attempts that were contact or non-contact using computer vision. Even though these efforts, there were few good results in this research. However, texture analysis is making a important role to solve these problems in various fields including universe, aviatiion, living thing and fibers. In this study, texture parameter was obtained by means of position operator according to variation of angle direction and distance. As a result, it was found that surface texture was more effected by direction then distance

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.102-106
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• 2005

As long as the effective contributions of satellite images in the continuous monitoring of the wide area and long range of time period, Landsat TM and Landsat ETM+ satellite images are surveyed. After quantization and classification of the deviations between TM and ETM+ images based on approved thresholds such as gains and biases or offsets, a correlation analysis method for the compared calibration is suggested in this paper. Four time points of raster data for 15 years of the highest group of land surface temperature and the lowest group of vegetation of the Kunsan city Chollabuk_do Korea located beneath the Yellow sea coast, are observed and analyzed their correlations for the change detection of urban land cover. This experiment based on proposed algorithm detected strong and proportional correlation relationship between the highest group of land surface temperature and the lowest group of vegetation index which exceeded R=(+)0.9478, so the proposed Correlation Analysis Model between the highest group of land surface temperature and the lowest group of vegetation index will be able to give proof an effective suitability to the land cover change detection and monitoring.