In the electronics industry, a lot of research and development is being conducted on electronic component supply, component alignment and insertion, and automation of soldering on the back side of the PCB for automatic PCB assembly. Additionally, as the use of electronic components increases in the automotive component field, there is a growing need to automate the alignment and insertion of components with leads such as transistors, coils, and fuses on PCB. In response to these demands, the types of PCB and parts used have been more various, and as this industrial trend, the quantity and placement of automation equipment that supplies, aligns, inserts, and solders components has become important in PCB manufacturing plants. It is also necessary to reduce the pre-setting time before using each automation equipment. In this study, we propose a method in which a vision system recognizes the type of component and simultaneously corrects alignment errors during the process of aligning and inserting various types of electronic components. The proposed method is effective in manufacturing various types of PCBs by minimizing the amount of automatic equipment inserted after alignment with the component supply device and omitting the preset process depending on the type of component supplied. Also the advantage of the proposed method is that the structure of the existing automatic insertion machine can be easily modified and utilized without major changes.

This paper presents the design and experimental analysis of a cryogenic refrigeration system for -100 ℃, primarily intended for semiconductor etching process. The refrigeration system utilizes non-flammable mixed refrigerant Joule-Thomson refrigeration cycle, incorporating a precooling stage to enhance overall performance. The selected refrigerants for the system include R1234yf for the precooling stage, and Ar, R14, R23 and R218 for the main cooling stage of the Joule-Thomson refrigeration cycle. Design results according to the system constraints and experimental results are discussed, including lowest evaporation temperature, compressor isentropic efficiency and overall pressure tendencies. The achieved refrigerant fraction from optimal design is Ar: R14: R23: R218 = 0.15: 0.4: 0.15: 0.3, indicating COP of 0.1118 at the isentropic compressor efficiency of 50%. The experimental result shows the developed system reaches steady state in approximately 3 hours.

A Electrostatic chuck is a device that fixes the substrate, using the force between charges applied between two parallel plates to attract substrates such as wafers or OLED panels. Unlike mechanical suction methods, which rely on physical fixation, this method utilizes the force of electrostatics for fixation, making it important to verify the adhesion force. As the size of the substrate increases, deformations due to gravity or chucking force also increase, and the adhesion force decreases rapidly as the distance between the chuck and the substrate increases. The outlook for displays is shifting from small to large OLEDs, necessitating consideration of substrate deformations. In this paper, to confirm the deformation of the substrate through various patterns, a simplified 2D model using Ansys' electromagnetic field analysis program, Maxwell, and the static structural analysis program, Mechanical, was utilized to observe changes in adhesion force according to the variation in the air gap between the substrate and the chuck. Additionally, the chucking force was analyzed for the size of the substrate, and the deformation of the substrate was confirmed when gravity and chucking force act simultaneously.

EtherCAT is an Ethernet-based fieldbus system standardized in IEC 61158 and SEMI, and widely used in the fields of factory automation, semiconductor equipment and robotics. In this paper, we summarize the current status of Xenomai real-time framework and RTnet, which are essential for Linux operating systems to operate in real-time, and implement a real-time EtherCAT master system with these open sources. The real-time performance of the implemented EtherCAT master is evaluated according to Intel network controllers 82574L, I219, I210, and I225, respectively. The results show that the implemented EtherCAT master provides precise control performance for control frequencies from 1KHz to 8KHz and similar performance for I219, I210, and I225, and relatively slightly larger jitter for 82574L.

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

The personal mobility Sharing service is currently active. Especially, electric scooters are widely utilized because they can move comfortably at a high speed over a short distance with a simple driving method. Its driving method is easy, but there is no protection device to protect the bare body. So, there is a greater accident than other means of transportation, and if two people are on board, there is higher accident probability. However, since there is no specific ways to prevent multi-person boarding yet, we propose a multi-person boarding detection model using tilt and pressure sensor. The proposed method measures the tilt degree and direction by using a tilt sensor installed in the center of the board plate and detects multi-people riding.

Recently, wearable virtual reality devices are widely used. These instruments include a 3-axis accelerometer. User's heart rate information in virtual reality contents can be useful for measuring user experience. In this paper, we propose a method to measure the heart rate through a 3-axis accelerometer based on the principle of ballistocardiography without additional sensors. The angular velocity was successively measured in a time series by the 3-axis accelerometer mounted to the head. The frequency of the maximum magnitude is determined as the heart rate through frequency transform and band pass filtering of the time series signal. For verification, the heart rate calculated from photoplethysmography sensors acquired at the same time was compared as ground-truth. In the virtual reality, the user's heart rate information can be extracted without additional heart rate sensor, and the emotional state and fatigue can be measured.

This research proposed DBSCAN-D, which is a clustering technique for locating POI based on existing density-based clustering research, such as GPS data, generated by moving objects. This method is designed based on 'staying time' and 'directionality' extracted from the relationship between GPS data. The staying time can be extracted through the difference in the reception time between data using the time at which the GPS data is received. Directionality can be expressed by moving the area of data generated later in the direction of the position of the previously generated data by concentrating on the point where the GPS data is sequentially generated. Through these two properties, it is possible to perform clustering suitable for the data set generated by the moving object.

Blockchain has gone beyond the proof-of-concept level and is converging with various industries and fields, moving toward the service development and commercialization stage. However, although various blockchain technologies and services are emerging, their development is quite slow and their widespread application to various industries is difficult. Accordingly, it is necessary to identify areas with high introduction utility when applying blockchain services in actual industries and to develop a method to evaluate the utility of blockchain services for this purpose. This paper proposes a framework for evaluating the utility of blockchain services using a multi-criteria decision-making model. Through a case study on the utility evaluation of blockchain services, the proposed framework was applied to domestic and foreign blockchain services to evaluate its utility and verify its applicability. It is expected that the proposed framework will be able to identify industrial and functional characteristics where actual blockchain services can be introduced and demonstrate effective utility and can be used to develop blockchain services in various industrial fields.

In this paper, we propose a theoretical method to systematically analyze the power conversion efficiency of a single-phase PFC switching rectifier. Boost-type PFC was organized in order of highest correlation with load current using steady-state analysis results and introduced the concept of loss factor. The loss factors for each major element are summarized and presented in a table. This paper makes it easier to understand the internal loss and power conversion efficiency of the rectifier for loss factors. Lastly, to confirm the validity of the efficiency analysis results reflecting the loss factors, loss and efficiency analysis of the 2.5kW PFC rectifier was performed. The results were compared with data from a 2.5kW PFC circuit for evaluation. As a result, the usefulness of power conversion efficiency analysis reflecting the loss factors proposed in this paper was confirmed.

Plasma Enhanced Chemical Vapor Deposition (PE-CVD) is a widely used technology in semiconductor manufacturing for thin film deposition. The implementation of wafer guide rings in PE-CVD processes is crucial for enhancing efficiency and product quality by ensuring uniform deposition around wafer edges and reducing particle generation. On the other hand, to prevent overall temperature non-uniformity and degradation of thin film quality within the chamber, it is essential to consider various parameters comprehensively. In this study, after applying the wafer guide rings, temperature variations and fluid flow changes were simulated. Additionally, by simulating the temperature and flow changes when applied to the PE-CVD chamber, this paper discusses the importance of optimizing variables within the entire chamber.

The threshold voltage shift in thin-film transistors (TFTs) is modeled using stretched-exponential (SE) and stretched-hyperbola (SH) functions. These models are derived by introducing empirical parameters into reaction rate equations that describe defect generation or charge trapping caused by hydrogen diffusion in the dielectric or interface. Separately, the dielectric relaxation phenomena are also described by the same reaction rate equations based on defect diffusion. Dielectric relaxation was initially modeled using the SE model, and various models have been proposed using fractional calculus. In this study, the characteristics of the threshold voltage shift and the dielectric relaxation phenomena are compared and analyzed to explore the applicability of analytical models used in the field of dielectric relaxation, in addition to the conventional SE and SH models.

Using a computational fluid dynamics(CFD) simulation tool, we have provided a coating guideline for slot-die coating with a double layer slot die head. We have analyzed the fluid dynamics in terms of the coating speed, flow rate ratio, and viscosity ratio, which are critical for the stability of coating meniscus. We have identified the common coating defects such as break-up, air entrainment, and leakage by varying the coating speeds. The flow rate ratio is the critical parameter determining the wet film thickness of the top and bottom layers. It is shown that when the flow rate ratio exceeds or equals 1.8, air entrainment occurs due to insufficient hydraulic pressure in the bottom layer, even though the total flow rate remains constant. Furthermore, we have found that the flow of the bottom layer is significantly affected by the viscosity of top layer. The viscosity ratio of 4 or higher obstructs the flow of the bottom layer due to the increased hydraulic resistance, resulting in leakage. Finally, we have demonstrated that as the viscosity ratio increases from 0.1 to 10, the maximum coating speed rises from 0.4 mm/s to 1.6 mm/s, and the minimum wet film thickness decreases from 800 ㎛ to 200 ㎛.

This research focuses on processing unstructured data efficiently, containing various formulas in document processing and management regarding the terms and rules of domestic insurance documents using text mining techniques. Through parsing and compilation technology, document context, content, constants, and variables are automatically separated, and errors are verified in order of the document and logic to improve document accuracy accordingly. Through document debugging technology, errors in the document are identified in real time. Furthermore, it is necessary to predict the changes that intelligent document processing will bring to document management work, in particular, the impact on documents and utilization tasks that are double managed due to various formulas and prepare necessary capabilities in the future.

UC Berkeley developed RISC-V, which is an open-source Instruction Set Architecture. This paper proposes a 32-bit 6-stage pipeline architecture based on the RV32I RSIC-V. The performance of the proposed 6-stage pipeline architecture is compared with the existing 32-bit 5-stage pipeline architecture also based on the RV32I processor ISA to determine the impact of the number of pipeline stages on performance. The RISC-V processor is designed in Verilog-HDL and implemented using Quartus Prime 20.1. To compare performance the Dhrystone benchmark is used. Subsequently, peripherals such as GPIO, TIMER, and UART are connected to verify operation through an FPGA. The maximum clock frequency for the 5-stage pipeline processor is 42.02 MHz, while for the 6-stage pipeline processor, it was 49.9MHz, representing an 18.75% increase.

With the growing field and growing interest in transparent organic light-emitting diodes (TOLED) in the industry, various attempts are being made to improve the transmittance and performance of TOLED. TOLEDs are expected to be used in next-generation displays such as mixture reality (MR) displays, displayable windows, televisions, etc. This study presents a mesh TOLED with better transmittance and luminescence characteristics than existing TOLEDs through an in-situ vacuum deposition method that does not require additional processes such as photolithography and etching. In this study the mesh TOLED's cathode consists of Mg: Ag 1:9 electrode. Mesh patterns are interconnected with a 6 nm layer of interlayer. We approached transmittance improvement up to 30% at 555 nm at the cathode electrode with similar current injection character, also we improved lumination characteristics up to 23% at 7 V driving condition.

SiO₂, renowned for its excellent insulating properties, has been used in the semiconductor industry as a valuable dielectric material. High-quality SiO₂ films find applications in gate spacers and interlayer insulation gap-fill oxides, among other uses. One of the prevalent methods for depositing these SiO₂ films is plasma enhanced chemical vapor deposition (PECVD) favored for its relatively low processing costs and ability to operate at low temperatures. However, compared to the increasingly utilized atomic layer deposition (ALD) method, PECVD exhibits inferior film characteristics such as uniformity. This study aims to produce SiO₂ films with uniformity as close as possible to those achieved by ALD through the adjustment of PECVD process parameters. we conducted a total of nine PECVD processes, varying the process time and gas flow rates, which were identified as the most influential factors on the PECVD process. Furthermore, ellipsometry analysis was employed to examine the uniformity variations of each process. The experimental results enabled us to elucidate the relationship between uniformity and deposition rate, as well as the impact of gas flow rate and deposition time on the process outcomes. Additionally, thickness measurements obtained through ellipsometer facilitate the identification of optimal process parameters for PECVD.

As semiconductor processes require several nanometers precision, the importance of motor control is increasing in semiconductor equipment. Due to unpredictable uncertainties such as friction and mechanical vibrations achieving precise position control in semiconductor processes is challenging. The internal model principle-based controller is a control technique that ensures robust steady-state performance by incorporating a model of the reference and disturbance. The disturbance observer-based controller is a prominent robust control technique implemented to cope with various nonlinearities and uncertainties. Provided that the two controllers can be designed to exhibit equivalent performance under certain conditions, this paper demonstrates through experiments that they yield identical results for the case of a BLDC position control problem. The experimental results also indicate that they can offer enhanced robustness compared with the conventional PID controller in the presence of a time-varying disturbance.

To generate the plasma required in dry cleaning processes, the plasma chamber must be supplied with a high-quality AC voltage with a voltage of more than 1 kV and a frequency of 400 kHz. In the existing research, many methods to supply high power have been studied, but how to improve the quality of the power for high-quality plasma has been relatively little studied. In this paper, we propose a study to improve the quality of RF power circuit for high-quality plasma generation in dry cleaning method. Existing methods in the environment of full-bridge-based RF power circuits must perform PWM duty control in the light load region. This causes distortions in the waveform, resulting in poor power quality, which directly leads to poor plasma quality. To solve these problems, a half-bridge switching method is proposed and the improvement in waveform quality is verified. To verify the feasibility of the design and control algorithm proposed in this paper, an RF power circuit prototype is fabricated and the proposed design and control method is verified through simulation and actual experiments under dummy load.

Scheduling of semiconductor test facilities has been crucial since effective scheduling contributes to the profits of semiconductor enterprises and enhances the quality of semiconductor products. This study aims to solve the scheduling problems for the wafer burn-in test facilities of the semiconductor back-end process by utilizing simulation and deep reinforcement learning-based methods. To solve the scheduling problem considered in this study. we propose novel state, action, and reward designs based on the Markov decision process. Furthermore, a neural network is trained by employing the recent RL-based method, named proximal policy optimization. Experimental results showed that the proposed method outperformed traditional heuristic-based scheduling techniques, achieving a higher due date compliance rate of jobs in terms of total job completion time.

NF₃, Chamber cleaning gas, has a high Global Warming Potential (GWP) of 17,000, causing significant greenhouse effects. Reducing gas usage during the cleaning process is crucial while increasing the cleaning Rate and reducing cleaning standard deviation (Stdev). In a previous study with a 6-inch PECVD chamber, a multiple linear regression analysis showed that Power and Pressure had no significant effect on the cleaning Rate because of their P-values of 0.42 and 0.68. The weight for Flow is 11.55, and the weights for Power and Pressure are 1.4 and 0.7. Due to the limitations of the research equipment, which differed from those used in actual industrial settings, it was challenging to assess the effects in actual industrial environment. Therefore, to show an actual industrial environment, we conducted the cleaning process on a 12-inch PECVD chamber, which is production-level equipment, and quantitatively analyzed the effects of each variable. Power, Pressure, and NF3 Flow all had P-values close to 0, indicating strong statistical significance. The weight for Flow is 15.68, and the weights for Power and Pressure are 4.45 and 5.24, respectively, showing effects 3 and 7 times greater than those with the 6-inch equipment on the cleaning rate. Additionally, we analyzed the cleaning Stdev and derived that there is a trade-off between increasing the cleaning Rate and reducing the cleaning Stdev.

Major countries are trying to expand the construction of smart factories by introducing ICT such as the Internet of Things, cloud, and big data into the manufacturing sector to secure national-level manufacturing competitiveness in the era of the 4th industrial revolution. In addition, Germany is pushing for Industry 4.0 to build a fully automatic production system through the Internet of Things, and China is pushing for the expansion of smart factories to enhance the country's industrial competitiveness through Made in China 2025, Japan's intelligent manufacturing system, and the Korean government's manufacturing innovation 3.0. In this study, considering the increasing security connectivity of smart factories, we would like to identify security threats in the external connection part of smart factories and suggest security enhancement measures based on domestic and international standard security models to respond to the identified security threats. Eventually the proposed method can be applied by accurately identifying the smart factory security status, diagnosing vulnerabilities, establishing appropriate improvement plans, and expanding security strategies to respond to security threats.

This study aims to talk about the necessity of solving the PFC gas emission problem raised by the recent development of the semiconductor industry and the remote plasma source method monitoring system used in the semiconductor industry. The 'monitoring system' means that the researchers applied machine learning to the existing monitoring technology and modeled it. In the process of this study, Residual Gas Analyzer monitoring technology and linear regression model were used. Through this model, the researchers identified emissions of at least 12700mg CO2 to 75800mg CO2 with values ranging from ion current 0.6A to 1.7A, and expect that the 'monitoring system' will contribute to the effective calculation of greenhouse gas emissions in the semiconductor industry in the future.

Cu-Cu bonding, one of the key technologies in advanced packaging, enhances semiconductor chip performance, miniaturization, and energy efficiency by facilitating rapid data transfer and low power consumption. However, the quality of the interface bonding can significantly impact overall bond quality, necessitating strategies to quickly detect and classify in-process defects. This study presents a methodology for detecting defects in wafer junction areas from Scanning Acoustic Microscopy images using a ResNet-50 based deep learning model. Additionally, the use of the defect map is proposed to rapidly inspect and categorize defects occurring during the Cu-Cu bonding process, thereby improving yield and productivity in semiconductor manufacturing.