• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.1-5
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• 2016

In order to apply sub-resolution assist feature (SRAF) in extreme ultraviolet lithography, the maximum non-printing SRAF width and lithography process margin needs to be improved. Through simulation, we confirmed that the maximum SRAF width of 6% attenuated phase shift mask (PSM) is large compared to conventional binary intensity mask. The increase in SRAF width is due to dark region's reflectivity of PSM which consequently improves the process window. Furthermore, the critical dimension error caused by variation of SRAF width and center position is reduced by lower change in diffraction amplitude. Therefore, we speculate that the margin of SRAF application will be improved by using PSM.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.40-47
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• 2009

In this paper, we present the design of a source driver of QVGA scale TFT-LCD driver IC which uses a mixed driving method and performs $\gamma$-correction to improve image. The source driver with 240 RGB ${\times}$ 320 dots resolution drives a TFT-LCD panel through 720 channels and implements 262k colors using 18-bit RGB data format. The mixed driving method is a mixture the channel amp. driving method with high drivability and the gray amp. driving method with small area, which remarkably reduces channel driver areas. The driver has been designed using the $0.35{\mu}m$ Magnachip embedded DRAM technology and simulated using the HSPICE simulator. The results show that our source driver operates well with y-correction and the channel driver has $17{\mu}s$ channel driving time with only 78 driving amplifiers and control logic.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.487-499
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• 2009

Atmospheric correction of satellite measurements is a major step to estimate accurate surface reflectance of solar spectrum channels. In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model used to retrieve surface reflectance from MODIS (MODerate resolution Imaging Spectrometer) top of atmosphere (TOA) reflectance. It is fast and simple atmospheric correction method, so it uses for work site operation in various satellite. This study attempts a test of accuracy of SMAC through a sensitivity test to detected error sources and to improve accuracy of surface reflectance using SMAC. The results of SMAC as compared with MODIS surface reflectance (MOD09) was represented that low accuracy ($R^2\;=\;0.6196$, Root Means Square Error (RMSE) = 0.00031, bias = - 0.0859). Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Among the input parameters, Aerosol Optical Depth (AOD) is the most influence input parameter. In order to modify AOD term in SMAC code, Stepwise multiple regression was performed with testing and remove variable in three stages with independent variables of AOD at 550nm, solar zenith angle, viewing zenith angle. Surface reflectance estimation by using Newly proposed AOD term in the study showed that improve accuracy ($R^2\;=\;0.827$, RMSE = 0.00672, bias = - 0.000762).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1299-1312
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• 2019

As land remote sensing applications are expanding to the extraction of quantitative information, the importance of atmospheric correction is increasing. Considering the difficulty of atmospheric correction for land images, it should be applied when it is necessary. The quantitative information extraction and time-series analysis on biophysical variables in land surfaces are two major applications that need atmospheric correction. Atmospheric aerosol content and column water vapor, which are very dynamic in spatial and temporal domain, are the most influential elements and obstacles in retrieving accurate surface reflectance. It is difficult to obtain aerosol and water vapor data that have suitable spatio-temporal scale for high- and medium-resolution multispectral imagery. Selection of atmospheric correction method should be based on the availability of appropriate aerosol and water vapor data. Most atmospheric correction of land imagery assumes the Lambertian surface, which is not the case for most natural surfaces. Further BRDF correction should be considered to remove or reduce the anisotropic effects caused by different sun and viewing angles. The atmospheric correction methods of optical imagery over land will be enhanced to meet the need of quantitative remote sensing. Further, imaging sensor system may include pertinent spectral bands that can help to extract atmospheric data simultaneously.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.161-161
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• 2023

Bias correction of values is a necessary step in downscaling coarse and systematically biased global climate models for use in local climate change impact studies. In addition to univariate bias correction methods, many multivariate methods which correct multiple variables jointly - each with their own mathematical designs - have been developed recently. While some literature have focused on the inter-comparison of these multivariate bias correction methods, none have focused extensively on the effect of diverse configurations (i.e., different combinations of input variables to be corrected) of climate variables, particularly high-dimensional ones, on the ability of the different methods to remove biases in uni- and multivariate statistics. This study evaluates the impact of three configurations (inter-variable, inter-spatial, and full dimensional dependence configurations) on four state-of-the-art multivariate bias correction methods in a national-scale domain over South Korea using a gridded approach. An inter-comparison framework evaluating the performance of the different combinations of configurations and bias correction methods in adjusting various climate variable statistics was created. Precipitation, maximum, and minimum temperatures were corrected across 306 high-resolution (0.2°) grid cells and were evaluated. Results show improvements in most methods in correcting various statistics when implementing high-dimensional configurations. However, some instabilities were observed, likely tied to the mathematical designs of the methods, informing that some multivariate bias correction methods are incompatible with high-dimensional configurations highlighting the potential for further improvements in the field, as well as the importance of proper selection of the correction method specific to the needs of the user.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.8
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• pp.47-55
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• 2010

In this paper, a timing register architecture using demultiplexer and counter is proposed to reduce the area of the high resolution SAR type analog to digital converter. The area and digital power consumption of the conventional timing register based on the shift register is drastically increased, as the resolution is increased. On the other hand, the proposed architecture results in reduction of the area and the power consumption of the error correction logic of the SAR ADC. This chip is implemented with 0.18 um CMOS process. The area is reduced by 5.4 times and the digital power consumption is minimized compared with the conventional one. The 12 bits SAR ADC shows ENOB of 11 bits, power consumption of 2 mW, and conversion speed of 1 MSPS. The die area is $1 mm{\times}1mm$.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.1-5
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• 2015

In photolithography process, resolution enhancement techniques such as optical proximity correction (OPC) and phase shift mask (PSM) have been applied to improve resolution. Especially, sub-resolution assist feature (SRAF) is one of the most important OPC to enhance image quality including depth of focus (DOF). However, imaging performance of the mask could be varied with the diffraction order amplitude changed by inserting SRAF. Therefore, in this study, we investigated the imaging properties and process margin of attenuated PSM with SRAF. Reflectivities of attenuated PSMs at 13.5 nm were 3, 6, 9% and simulation was performed by $PROLITH^{TM}$. As a result, aerial image properties and DOF as well as diffraction efficiency were improved by increasing the reflectivity of attenuated PSM. Additionally, printed critical dimension variations depending on SRAF width and space error were also reduced for attenuated PSM with high reflectivity. However, SRAF could be printed when reflectivity of attenuated PSM is high enough. In conclusion, optimization of reflectivity of attenuated PSM and SRAF to prevent side-lobe from being printed is needed to be considered.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.