We presented a micro hemi-sphere structure flexible film to improve the external quantum efficiency (EQE) in OLEDs. The micro hemi-sphere flexible film was fabricated with breath figure (BF) method and replica process. At 45 mg/mL of concentration, the size of the hemi-spheres was approximately 6.2 ㎛ were obtained which are the most circular shape. So, it was possible to yield the best performance with an improvement of 33 % in the EQE and the widest viewing angle ranging from 0° to 70°. As a result, the hemi-sphere film's size and distribution seem to play important roles in enhancing the EQE in OLEDs. Furthermore, the flexible hemi-sphere film based on polymeric materials could offer an effective, large-scale, mass-produced product and a simple process and approach to achieve high efficiency in flexible OLEDs.

The electrostatic phenomenon seriously issued in extreme ultraviolet semiconductor cleaning was studied in junction with molecular dynamic aspect. It was understood that two lone pairs of electrons in water molecule were subtly different each other in molecular orbital symmetry, existed as two states of large energy difference, and became basis for water clustering through hydron bonds. It was deduced that when hydrogen bond formed by lone pair of higher energy state was broken, two types of [H₂O]⁺ and [H₂O]^- ions would be instantaneously generated, or that lone pair of higher energy state experiencing reactions such as friction with Teflon surface could cause electrostatic generation. It was specifically observed that, in case of electrolyzed cathode water, negative electrostatic charges by electrons were overlapped with negative oxidation reduction potentials without mutual reaction. Therefore, it seemed that negative electrostatic development could be minimized in cathode water by mutual repulsion of electrons and [OH]^- ions, which would be providing excellences on extreme ultraviolet cleaning and electrostatic control as well.

Due to the development of IT technology and the on-going Coronavirus disease, non-face-to-face services have been activated. To overcome the inconvenience of non-face-to-face service, service providers have adopted chatbots as a way to feel like a human being. As the increasing chatbot services, chatbot builders have emerged, which can help non-developers to build them. Although its popularity has increased, its performance evaluation has not been conducted on such chatbot builders. In this paper, we implement a prototype chatbot that classifies hospital departments in the medical field using Dialogflow and Rasa, which are popular chatbot builders. By measuring the accuracy of the chatbot's classification of medical subjects, we evaluated the level of accuracy that the most used chatbot builder can have when they are used to build a chatbot service. The simulation results showed that Dialogflow had 87%, 65%, and 60%, and Rasa did 64%, 70%, and 63% in surgery dermatology, and otolaryngology, respectively.

Photovoltaics (PV) power generation efficiency is affected by meteorological factors such as temperature and wind speed. In general, it is known that the power generation amount decreases because photovoltaics panel temperature rises and the power generation efficiency decreases in summer. Photovoltaics Thermal (PVT) power generation has the ad-vantage of being able to produce heat together with power, as well as preventing the reduction in power generation efficien-cy and output due to the temperature rise of the panel. In this study, the amount of heat collected by season and time was calculated for photovoltaics thermal modules using the International Weather for Energy Calculations (IWEC) data provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). Based on this, we propose a method of predicting the temperature of the photovoltaics panel using thermal analysis and then calculating the flow rate of coolant to improve power generation efficiency. As the results, the photovoltaics efficiencies versus time on January, April, July, and October in Jeju of the Republic of Korea were calculated to the range of 15.06% to 17.83%, and the maxi-mum cooling load and flow rate for the photovoltaics thermal module were calculated to 121.16 W and 45 cc/min, respec-tively. Though this study, it could be concluded that the photovoltaics thermal system can be composed of up to 53 modules with targeting the Jeju, since the maximum capacity of the coolant circulation pump of the photovoltaics thermal system applied in this study is 2,400 cc/min.

In this paper, our ray tracing hardware is implemented on the latest high-capacity FPGA board. The system included ray tracing hardware for rendering and tree building hardware for handling dynamic scenes. The FPGA board used in the implementation is a Xilinx Alveo U250 accelerator card for data centers. This included 12 ray tracing hardware cores and 1 tree-building hardware core. As a result of testing in various scenes in Full HD resolution, the FPS performance of the proposed ray tracing system was measured from 8 to 28. The overall average is about 17.7 FPS.

RISC-V is an open instruction set architecture (ISA) developed in 2010 at UC Berkeley, and active research is being conducted as a processor to compete with ARM. In this paper, we propose an SoC system including an RV32I ISA-based 32-bit 5-stage pipeline processor and AHB bus master. The proposed RISC-V processor supports 37 instructions, excluding FENCE, ECALL, and EBREAK instructions, out of a total of 40 instructions based on RV32I ISA. In addition, the RISC-V processor can be connected to peripheral devices such as BRAM, UART, and TIMER using the AHB-lite bus protocol through the proposed AHB bus master. The proposed SoC system was implemented in Arty A7-35T FPGA with 1,959 LUTs and 1,982 flip-flops. Furthermore, the proposed hardware has a maximum operating frequency of 50 MHz. In the Dhrystone benchmark, the proposed processor performance was confirmed to be 0.48 DMIPS.

With an attempt to suppress the moiré phenomenon caused by the interference between the black matrix of a display panel and the metal grid of a camera, we have fabricated an anti-moiré filter using light diffusing particles (LDPs) with the average diameter of 20 ㎛. It is demonstrated that the anti-moiré filter coated on a glass substrate (370 mm × 470 mm) using a table slot-die coater reduces the moiré intensity to a great extent when the area covered by LDPs is 50%. To quantify the intensity of moiré phenomenon, we have measured the lightness ratio and found that it is reduced from 132.12 down to 105.71 by the filter. To find the optimum area covered by LDPs, we have performed ray tracing simulations using Mie scatters as a substitute for LDPs. From the simulated irradiation distribution, we have calculated the standard deviation (SD) and contrast ratio (CR) to evaluate the moiré strength. As expected, the SD and CR values decrease with increasing covered area by LDPs. However, there exists a trade-off between the transmittance of the filter and its capability of reducing the moiré intensity in determining the area covered by LDPs.

Using a CFD (computational fluid dynamics) simulation tool, we have offered a design guideline of a slot-die head having a simple T-shaped cavity through an analysis of the fluid dynamics in terms of cavity pressure and outlet velocity, which affect the uniformity of coated thin films. We have visualized the fluid flow with a transparent slot-die head where poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS) is injected. We have shown that the fluid dynamics inside the slot-die head depends sensitively on the cavity depth, cavity length, land length, and channel gap (i.e., shim thickness). Of those, the channel gap is the most critical parameter that determines the uniformity of the pressure and velocity distributions. A pressure drop inside the cavity is shown to be reduced with decreasing shim thickness. To quantify it, we have also calculated the coefficient of variation (CV). In accordance with Hagen-Poiseuille's laws and electron-hydraulic analogy, the CV value is decreased with increasing cavity depth, cavity length, and land length.

Through Silicon Via (TSV) is a technology that interconnects chips through silicon vias. TSV technology can achieve shorter distance compared to wire bonding technology with excellent electrical characteristics. Due to this characteristic, it is currently being used in many fields that needs faster communication speed such as memory field. However, there is performance degradation issue on TSV technology due to the parasitic capacitance. To deal with this problem, in this study, the parasitic capacitance with TSV design factors is analyzed using commercial tool. TSV design factors were set in three categories: size, aspect ratio, pitch. Each factor was set by dividing the range with TSV used for memory and package. Ansys electronics desktop 2021 R2.2 Q3D was used for the simulation to acquire parasitic capacitance data. DOE analysis was performed based on the reaction surface method. As a result of the simulation, the most affected factors by the parasitic capacitance appeared in the order of size, pitch and aspect ratio. In the case of memory, each element interacted, and in the case of package, it was confirmed that size * pitch and size * aspect ratio interact, but pitch * aspect ratio does not interact.

Brownish rings (BRs) with white interiors are formed during the manufacture of silicon ingots in quartz glass crucibles. These BRs inhibit the yield of silicon ingots. However, the composition and mechanism of the formation of these BRs remain unclear thus far. Therefore, in this study, we analyzed the color and shape of these BRs. Raman analysis revealed that the brown and white colors appear owing to oxygen deficiency rather than crystallization from excess oxygen supply as previously assumed. Moreover, the dark shade of the brown areas depends on the degree of oxygen deficiency and the asymmetrical width of the brown areas is attributed to the direction of the molten silicon flow, which is influenced by the rotation and heat of the ingot crucible.

Shape changes of hard mask play a key role in the aspect ratio dependent etch (ARDE). For etch process using high density and energy ions, deformation of hard mask shape becomes more severe, and high aspect ratio (HAR) etch profile is distorted. In this study, polygonal geometric model for shape-deformation of amorphous carbon layered hard mask is suggested to control etch profile during the process. Mask shape is modeled with polygonal geometry consisting of trapezoids and rectangles, and it provides dynamic information about angles of facets and etched width and height of remained mask shape, providing important features for real-time HAR etch profiling.

Vibration is one of the factors that degrades the performance of equipment and measurement equipment used in high-tech industries such as semiconductors and display. The vibration isolator is classified into passive type and active type. The passive vibration isolator has the weakness of insufficient vibration isolation performance in the low frequency band, so an active vibration control system that can overcome these problems is used recently. In this paper, PID controller is used to control the active vibration isolator. Methods for setting the gain of the PID controller include the Zeigler-Nichols method, the pole placement method. These methods have the disadvantage of requiring a lot of time or knowing the system model accurately. This paper proposes the gain auto tuning method of the active vibration isolator applied with the PSO algorithm, which is an optimization algorithm that is easy to implement and has stable convergence performance with low calculations. It is expected that it will be possible to improve vibration isolation performance and reduce the time required for gain tuning by applying the proposed PSO algorithm to the active vibration isolator.

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

We evaluated and developed a 6th generation large-size polisher in the type of face-up and Oscar. We removed the hillocks of the low temperature poly-silicon (LTPS) thin film with this polisher. The surface roughness of LTPS was lowered from 7.9 nm to 0.6 nm after CMP(chemical mechanical polishing). The thickness of the LTPS is measured through reflectance in real time during polishing, and the polishing process is completed according to this thickness. The within glass non-uniformity (WIGNU) was 6.2% and the glass-to-glass non-uniformity (GTGNU) was 2.5%, targeting the LTPS thickness of 400Å. In addition, the residual slurry after the CMP process was removed through the Core Flow PVA Brush and alkaline chemical.

Automatic Vision Inspection (AVI) systems automatically detect defect features and measure their sizes via camera vision. It has been populated because of the accuracy and consistency in terms of QC (Quality Control) of inspection processes. Also, it is important to predict the performance of an AVI to meet customer's specification in advance. AVI are usually suffered from false negative and positives. It can be overcome by providing extra information such as 3D depth information. Stereo vision processing has been popular for depth extraction of the 3D images from 2D images. However, stereo vision methods usually take long time to process. In this paper, retro optical system using reflectors is proposed and experimented to overcome the problem. The optical system extracts the depth without special SW processes. The vision sensor and optical components such as illumination and depth detecting module are integrated as a unit. The depth information can be extracted on real-time basis and utilized and can improve the performance of an AVI system.

Mist-CVD is known to have advantages of low cost and high productivity method since the precursor solution is misting with an ultrasonic generator and reacted on the substrate under vacuum-free conditions of atmospheric pressure. However, since the deposition distribution is not uniform, various efforts have been made to derive optimal conditions by changing the angle of the substrate and the position of the outlet to improve the result of the preceding study. Therefore, in this study, a deposition distribution uniformity model was derived through the shape and position of the substrate support and the conditions of inlet flow rate using the particle tracking method of computational fluid dynamics (CFD). The results of analysis were compared with the previous studies through experiment. It was confirmed that the rate of deposition area was improved from 38.7% to 100%, and the rate of deposition uniformity was 79.07% which was higher than the predicted result of simulation. Particle tracking method can reduce trial and error in experiments and can be considered as a reliable prediction method.

Cache bypassing is a scheme to prevent unnecessary cache blocks from occupying the capacity of the cache for avoiding cache contamination. This method is introduced to alleviate the problems of non-volatile memories (NVMs)-based memory system. However, the prior works have been studied without considering wear-out attack. Malicious writing to a small area in NVMs leads to the failure of the system due to the limited write endurance of NVMs. This paper proposes a novel scheme to prolong the lifetime with higher resistance for the wear-out attack. First, the memory reference pattern is found by modified reuse distance calculation for each cache block. If a cache block is determined as the target of the attack, it is forwarded to higher level cache or main memory without updating the NVM-based cache. The experimental results show that the write endurance is improved by 14% on average and 36% on maximum.

This paper proposes a new method of the traffic signal detection and the recognition in an HSI color model. The proposed method firstly converts a ROI image in the RGB model to in the HSI model to segment the color of a traffic signal. Secondly, the segmented colors are dilated by the morphological processing to connect the traffic signal light and the signal light case and finally, it extracts the traffic signal light and the case by the aspect ratio using the connected component analysis. The extracted components show the detection and the recognition of the traffic signal lights. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the detection and the recognition of traffic signals.

Public attention to the indoor environment of underground subway stations, which is a representative multi-use facility, has been increasing along with the increase in indoor activities. In underground stations, fine iron oxide, which affects the health of users, is generated because of the friction between wheels and rails. Among particulate pollutant reduction technologies, plants have been considered as a non-chemical air purification method, and their effects in reducing certain chemical species have been identified in previous studies. The present study aimed to derive the total quantitative and qualitative reduction effects of a bio-filter system comprising air purifying plants, installed in an underground subway station. The experiment proceeded in two ways. First, PM(particulate matter) reduction effect by vegetation biofilter was monitored with the IAQ(indoor air quality) station. In addition, chemical speciation analysis conducted on the samples collected from the experimental and control areas where plants and irrigation using SEM-EDS(scanning electron microscopy-energy dispersive X-ray spectroscopy). This study confirmed the effect of the vegetation bio-filter system in reducing the accumulation of particulate pollutants and transition and other metals that are harmful to the human body.

The green-emitting Zn₂SiO₄:Mn²⁺ phosphor film was evaluated in a xenon excimer lamp. The phosphor film with 2 ㎛ thick was formed of monolithic structure on the inner side of quartz through a long-time annealing process of coated ZnO solution doped with Mn²⁺ ion and SiO₂ of quartz tube. The coated quartz was filled with 100 torr of xenon gas, and simultaneously both sides was melt and sealed. The xenon-field quartz tube was discharge by applying the voltage of 15 kV with a frequency of 26 kHz, and emitted the glow with dominant peak at 172 nm. The vacuum ultraviolet excited the inner-side coated Zn₂SiO₄:Mn²⁺ phosphor film, which emitted the pure and strong green light.

Accurate current-voltage modeling of solar cell systems plays an important role in power prediction. Solar cells have nonlinear characteristics that are sensitive to environmental conditions such as temperature and irradiance. In this paper, the output characteristics of photovoltaic module are accurately predicted by combining the artificial neural network and physical model. In order to estimate the performance of PV module under varying environments, the artificial neural network model is trained with randomly generated temperature and irradiance data. With the use of proposed model, the current-voltage and power-voltage characteristics under real environments can be predicted with high accuracy.

In this paper, we developed a paper blood glucose sensor that can minimize the effect of hematocrit. The paper blood glucose sensor has the advantage of being very simple in its production process as it is manufactured with only three printing processes on the top of the paper substrate. This glucose sensor consists of a total of six electrodes, including blood glucose measurement electrodes, hematocrit measurement electrodes, strip detection electrodes, and blood detection electrodes. A paper blood glucose sensor measures hematocrit with electrodes formed on the same sensor substrate when measuring blood glucose concentration, and compensates for the effect of hematocrit in real time to enable accurate blood glucose measurement.

This paper deals with the development of a deep-learning-based robot that recognizes various types of stairs and performs a mission to go up to the target floor. The overall motion sequence of the robot is performed based on the ROS robot operating system, and it is possible to detect the shape of the stairs required to implement the motion sequence through rapid object recognition through YOLOv4 and Cuda acceleration calculations. Using the ROS operating system installed in Jetson Nano, a system was built to support communication between Arduino DUE and OpenCM 9.04 with heterogeneous hardware and to control the movement of the robot by aligning the received sensors and data. In addition, the web server for robot control was manufactured as ROS web server, and flow chart and basic ROS communication were designed to enable control through computer and smartphone through message passing.

The number of joint disease patients is increasing every year. Currently, the most CPM(Continuous Passive Motion) equipment uses expensive imported equipment, and one CPM equipment is designed to be used only in one joint, medical personnel or hospitals who are the main users of the medical equipment need to have several types of CPMs for joint rehabilitation. To solve this problem, this paper designed a multifunctional joint medical equipment that enables rehabilitation of knee, shoulder, and elbow joints in one CPM equipment and includes general, intensive, and adaptive exercise functions for effective treatment according to the patient's condition. The patient's condition was diagnosed using a load cell and a current sensor. In this paper, effective rehabilitation methods were presented and high reliability and precision of medical equipment was confirmed through experiments using potentiometer, encoder, and PI controller.

In this paper, we have studied the quadrature error reduction for the single drive 3-axis MEMS Gyroscope. There was a limitation of the previous study which is the z-axis quadrature error was large. To reduce this value, design methodologies were presented. And the methodologies included a different mesh application, z-rate spring structure change, and mass compensation for balancing of the structure. We conducted the modal analysis, drive mode analysis and sense mode analysis using COMSOL Multiphysics. As a result, a drive resonant frequency was 26003 Hz, with the x-sense, y-sense, z-sense being 26749 Hz, 26858 Hz, 26920 Hz, respectively. And the Mechanical sensitivity was computed at 2000 degrees per second(dps) input angular rate while the sensitivity for roll, pitch, and yaw was computed 0.011, 0.012, and 0.011 nm/dps respectively. And z-axis quadrature error was successfully improved, 2.78 nm to 0.95 nm, which the improvement rate was about 66 %.

We analyzed the influence of post-annealing on Ga₂O₃/n-type 4H-SiC heterojunction diode. Gallium oxide (Ga₂O₃) thin films were deposited by radio frequency (RF) sputtering. Post-deposition annealing at 950℃ in an Oxygen atmosphere was performed. The material properties of Ga₂O₃ and the electrical properties of the diodes were investigated. Atomic Force Microscopy (AFM), X-Ray Diffraction and Scanning Electron Microscope (SEM) images show a significant increase in the roughness and crystallinity of the O₂-annealed films. After Oxygen annealing X-ray Photoelectron Spectroscopy (XPS) shows that the atomic ratio of oxygen increases which is related to a decrease in oxygen vacancy within the Ga₂O₃ film. The O₂-annealed diodes exhibited higher on-current and lower leakage current. Moreover, the ideality factor, barrier height, and thermal activation energy were derived from the current-voltage curve by increasing the temperature from 298 - 434K.

In this paper, we analyze the current research technology trends through the literature reviews of technical outlines of electric vehicles and hydrogen fuel cell vehicles, domestic and overseas policy trends, etc. After analyzing the literature, we found out while the re-use and recycling of waste batteries and the in-wheel motor systems are essential areas for the development of electric vehicles and hydrogen fuel cell vehicles, the related research is not quite sufficient, so the direction for further research is proposed at the conclusion.

In this paper, we analyze the current research technology trends through the literature reviews of technical outlines of electric vehicles and hydrogen fuel cell vehicles, domestic and overseas policy trends, etc. After analyzing the literature, we found out while the re-use and recycling of waste batteries and the in-wheel motor systems are essential areas for the development of electric vehicles and hydrogen fuel cell vehicles, the related research is not quite sufficient, so the direction for further research is proposed at the conclusion.

Image compression plays an important role in encoding and improving various forms of images in the digital era. Recent researches have focused on the principle of deep learning as one of the most exciting machine learning methods to show that it is good scheme to analyze, classify and compress images. Various neural networks are able to adapt for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks and convolution neural networks. In this review paper, we discussed how to apply the rule of deep learning to obtain better image compression with high accuracy, low loss-ness and high visibility of the image. For those results in performance, deep learning methods are required on justified manner with distinct analysis.

In this paper, an optimization design method of a diffuser using Bernoulli's theorem was reviewed. The aspect ratio of the cylindrical diffuser chamber and the diameter ratio of the air inlet and outlet were used as design parameters. For the optimal design of the cylindrical diffuser chamber, the air flow inside the chamber was simulated using ANSYS while changing the aspect ratio of the chamber. In order to confirm the simulation results, the diffuser manufactured using the laser processing machine was measured. Through ANSYS simulation and measurement, it was found that the optimal design condition was when the aspect ratio (chamber height/radius) of the diffuser chamber was 1/2 and the diameter ratio of the air inlet and outlet was also 1/2.

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.