• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.149-149
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• 2010

The double-layer antireflection (DLAR) coatings have significant advantages over single-layer antireflection (SLAR) coatings. This is because they will be able to cover a broad range of the solar spectrum which would enhance the overall performance of solar cells. Moreover films deposited at high frequency are expected to show excellent and UV-stable passivation in the refractive index that we adopted. In this work, we present a novel DLAR coating using SiNx:H thin films with refractive indices 1.9 and 2.3 as the top and bottom layers. This approach is cost effective when compared to earlier DLAR coatings with two different materials. SiNx:H films were deposited by Plasma enhanced chemical vapor deposition (PECVD) technique using $SiH_4$, $NH_3$ and $N_2$ gases with flow rates 20~80sccm, 200sccm and 85 sccm respectively. The RF power, plasma frequency and substrate temperature for the deposition were 300W, 13.56 MHz and $450^{\circ}C$, respectively. The optimum thickness and refractive indices values for DLAR coatings were estimated theoretically using Macleod simulation software as 82.24 nm for 1.9 and 68.58 nm for 2.3 respectively. Solar cells were fabricated with SLAR and DLAR coatings of SiNx:H films and compared the cell efficacy. SiNx:H> films deposited at a substrate temperature of $450^{\circ}C$ and that at 300 W power showed best effective minority carrier lifetime around $50.8\;{\mu}s$. Average reflectance values of SLAR coatings with refractive indices 1.9, 2.05 and 2.3 were 10.1%, 9.66% and 9.33% respectively. In contrast, optimized DLAR coating showed a reflectance value as low as 8.98% in the wavelength range 300nm - 1100nm.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.244.1-244.1
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• 2013

The p-type GaN which act as a hole injection layer in GaN-based LEDs has fundamental problems. The first one arises from the difficulty in growing a highly doped p-GaN (with a carrier concentration exceeding ~1018 $cm^{-3}$). And the second one is the absence of appropriate metals or conducting oxides having a work function that is larger than that of p-type GaN (7.5 eV). Moreover, the LED efficiency is decreases gradually as the injection current increases (the so-called 'efficiency droop' phenomenon). The efficiency droop phenomenon in InGaN quantum wells (QWs) has been a large obstacle that has hindered high-efficiency operation at high current density. In this study, we introduce the new approaches to improve the light-output power of LEDs by using graphene oxide sheets. Graphene oxide has many functional groups such as the oxygen epoxide, the hydroxyl, and the carboxyl groups. Due to nature of such functional groups, graphene oxide possess a lot of hole carriers. If graphene oxide combine with LED top surface, graphene oxide may supply hole carriers to p-type GaN layer which has relatively low free carrier concentration less than electron concentration in n-type GaN layer. To prove the enhancement factor of graphene oxide coated LEDs, we have investigated electrical and optical properties by using ultra-violet photo-excited spectroscopy, confocal scanning electroluminescence microscopy.

• Korean Journal of Optics and Photonics
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• v.1 no.1
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• pp.46-51
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• 1990

Laser plasma was generated by a 1GW iodine photodissociation laser ($\lambda$=1.315$\mu\textrm{m}$, E=12.7J) whose output beam was focused on a molybdenum target surface. The experiment was conducted in a vacuum chamber under 1D-sTorr and several tens of laser shooting were necessary for sufficient exposure for the PBS resist of 111m thickness. Aluminium was coated on the top of the resist by 0.1$\mu\textrm{m}$ thickness which acts as an X-ray filter to cut off the visible and the ultraviolet lights. A bio-specimen was put directly on the aluminium coated resist and located at a distance of 3 cm from the X-ray source. The replicas of a steel mesh, spider's web. and a red blood cell were obtained by this technique and were observed by Nomarski microscope and SEM. The limitation of its resolution is determined by the X-ray source size and Fresnel diffraction effect, and its theoretical prediction is well matched with the experimental results. In this experiment, a resolution better than 0.1$\mu\textrm{m}$ could be obtained. ained.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.4
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• pp.190-195
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• 2021

Typical Mn-SnO₂/Ag/Mn-SnO₂ tri-layer films were prepared on a PET substrate by RF/DC magnetron sputtering method at room temperature. Based on EMP simulation, the thicknesses of the top and bottom Mn-doped SnO₂ layers were kept at 40 nm and the Ag layer was maintained at 13 nm for continuous electrical conduction. The experimentally measured optical transmittances at 550 nm wavelength were ranged from 82.9 to 88.1 % and sheet resistances were varied from 5.9 to 6.9 Ω/☐. The highest value of figure of merit, ϕ_TC was 48.1 × 10^-3 Ω^-1. Based on bending test under 4 and 5 mm of inner and outer curvature radius condition, tri-layer film resistance varies only by approximately 1.5 % after 10,000 bending cycles, showing excellent mechanical flexibility.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.343-353
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• 2022

An attempt to derive the surface temperature from the Korea Multi-purpose Satellite (KOMPSAT)-3A mid-wave infrared (MWIR) data acquired over the southern California on Nov. 14, 2015 has been made using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model. Since after the successful launch on March 25, 2015, the KOMPSAT-3A spacecraft and its two payload instruments - the high-resolution multispectral optical sensor and the scanner infrared imaging system (SIIS) - continue to operate properly. SIIS uses the MWIR spectral band of 3.3-5.2 ㎛ for data acquisition. As input data for the realistic simulation of the KOMPSAT-3A SIIS imaging conditions in the MODTRAN model, we used the National Centers for Environmental Prediction (NCEP) atmospheric profiles, the KOMPSAT-3Asensor response function, the solar and line-of-sight geometry, and the University of Wisconsin emissivity database. The land cover type of the study area includes water,sand, and agricultural (vegetated) land located in the southern California. Results of surface temperature showed the reasonable geographical pattern over water, sand, and agricultural land. It is however worthwhile to note that the surface temperature pattern does not resemble the top-of-atmosphere (TOA) radiance counterpart. This is because MWIR TOA radiances consist of both shortwave (0.2-5 ㎛) and longwave (5-50 ㎛) components and the surface temperature depends solely upon the surface emitted radiance of longwave components. We found in our case that the shortwave surface reflection primarily causes the difference of geographical pattern between surface temperature and TOA radiance. Validation of the surface temperature for this study is practically difficult to perform due to the lack of ground truth data. We therefore made simple comparisons with two datasets over Salton Sea: National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) field data and Salton Sea data. The current estimate differs with these datasets by 2.2 K and 1.4 K, respectively, though it seems not possible to quantify factors causing such differences.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.99-120
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• 1997

Solar radiation is scattered and absorbed atmospheric compositions in the atmosphere before it reaches the surface and, then after reflected at the surface, until it reaches the satellite sensor. Therefore, consideration of the radiative transfer through the atmosphere is essential for the quantitave analysis of the satellite sensed data, specially at shortwave region. This study examined a feasibility of using radiative transfer code for estimating the atmospheric effects on satellite remote sensing data. To do this, the flux simulated by LOWTRAN7 is compared with CAGEX data in shortwave region. The CAGEX (CERES/ARM/GEWEX Experiment) data provides a dataset of (1) atmospheric soundings, aerosol optical depth and albedo, (2) ARM(Aerosol Radiation Measurement) radiation flux measured by pyrgeometers, pyrheliometer and shadow pyranometer and (3) broadband shortwave flux simulated by Fu-Liou's radiative transfer code. To simulate aerosol effect using the radiative transfer model, the aerosol optical characteristics were extracted from observed aerosol column optical depth, Spinhirne's experimental vertical distribution of scattering coefficient and D'Almeida's statistical atmospheric aerosols radiative characteristics. Simulation of LOWTRAN7 are performed on 31 sample of completely clear days. LOWTRAN's result and CAGEX data are compared on upward, downward direct, downward diffuse solar flux at the surface and upward solar flux at the top of the atmosphere(TOA). The standard errors in LOWTRAN7 simulation of the above components are within 5% except for the downward diffuse solar flux at the surface(6.9%). The results show that a large part of error in LOWTRAN7 flux simulation appeared in the diffuse component due to scattering mainly by atmispheric aerosol. For improving the accuracy of radiative transfer simulation by model, there is a need to provide better information about the radiative charateristrics of atmospheric aerosols.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.347-358
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• 2019

Lambertian cloud model (Lambertian Cloud Model) is the simplified cloud model which is used to effectively retrieve the vertical ozone distribution of the atmosphere where the clouds exist. By using the Lambertian cloud model, the optical characteristics of clouds required for radiative transfer simulation are parametrized by Optical Centroid Cloud Pressure (OCCP) and Effective Cloud Fraction (ECF), and the accuracy of each parameter greatly affects the radiation simulation accuracy. However, it is very difficult to generalize the vertical ozone error due to the OCCP error because it varies depending on the radiation environment and algorithm setting. In addition, it is also difficult to analyze the effect of OCCP error because it is mixed with other errors that occur in the vertical ozone calculation process. This study analyzed the ozone retrieval error due to OCCP error using two methods. First, we simulated the impact of OCCP error on ozone retrieval based on Optimal Estimation. Using LIDORT radiation model, the radiation error due to the OCCP error is calculated. In order to convert the radiation error to the ozone calculation error, the radiation error is assigned to the conversion equation of the optimal estimation method. The results show that when the OCCP error occurs by 100 hPa, the total ozone is overestimated by 2.7%. Second, a case analysis is carried out to find the ozone retrieval error due to OCCP error. For the case analysis, the ozone retrieval error is simulated assuming OCCP error and compared with the ozone error in the case of PROFOZ 2005-2006, an OMI ozone profile product. In order to define the ozone error in the case, we assumed an ideal assumption. Considering albedo, and the horizontal change of ozone for satisfying the assumption, the 49 cases are selected. As a result, 27 out of 49 cases(about 55%)showed a correlation of 0.5 or more. This result show that the error of OCCP has a significant influence on the accuracy of ozone profile calculation.

• Journal of the Microelectronics and Packaging Society
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• v.23 no.3
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• pp.21-29
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• 2016

In this paper, we developed a flexible transparent capacitive pressure sensor which can recognize X and Y coordinates and the size of force simultaneously by sensing a change in electrical capacitance. The flexible transparent capacitive pressure sensor was composed of 3 layers which were top electrode, pressure sensing layer, and bottom electrode. Silver nanowire(Ag NW)/poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) hybrid film was used for top and bottom flexible transparent electrode. The fabricated capacitive pressure sensor had a total size of 5 inch, and was composed of 11 driving line and 19 sensing line channels. The electrical, optical properties of the Ag NW/PEDOT:PSS and capacitive pressure sensor were investigated respectively. The mechanical flexibility was also investigated by bending tests. Ag NW/PEDOT:PSS exhibited the sheet resistance of $44.1{\Omega}/square$, transmittance of 91.1%, and haze of 1.35%. Notably, the Ag NW/PEDOT:PSS hybrid electrode had a constant resistance change within a bending radius of 3 mm. The bending fatigue tests showed that the Ag NW/PEDOT:PSS could withstand 200,000 bending cycles which indicated the superior flexibility and durability of the hybrid electrode. The flexible transparent capacitive pressure sensor showed the transmittance of 84.1%, and haze of 3.56%. When the capacitive pressure sensor was pressed with the multiple 2 mm-diameter tips, it can well detect the force depending on the applied pressure. This indicated that the capacitive pressure sensor is a promising scheme for next generation flexible transparent touch screens which can provide multi-tasking capabilities through simultaneous multi-touch and multi-force sensing.