Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.

In the era of the 4th Industrial Revolution, new technologies such as artificial intelligence, big data, cloud, Internet of Things, and blockchain are being developed and applied to new industries. Blockchain has the characteristics of decentralization, security, and transparency, so it can serve as a core technology for developing new growth industries. Blockchain is provided as BaaS (Blockchain as a Service), but it is not easy for users who are introducing or building blockchain to choose BaaS. In this study, we identify evaluation factors and develop a decision-making model using fuzzy theory and AHP for BaaS selection. Eventually we aim to help companies choose the best BaaS and develop and commercialize blockchain-based services by developing a new decision-making model for BaaS selection.

The accelerated pace of climate crisis due to continuous industrialization and greenhouse gas emissions necessitates sustainable solutions that simultaneously address mitigation and adaptation to climate change. Naturebased Solutions (NbS) have gained prominence as viable approaches, with Green Infrastructure being a representative NbS. Green Infrastructure involves securing green spaces within urban areas, providing diverse climate adaptation functions such as removal of various air pollutants, carbon sequestration, and isolation. The proliferation of Green Infrastructure is influenced by the quantification of improvement effects related to various projects. To support decision-making by assessing the climate vulnerability of Green Infrastructure, the U.S. Department of Agriculture (USDA) has developed i-Tree Tools. This study proposes a comprehensive evaluation approach for climate change adaptation types by quantifying the climate adaptation performance of urban Green Infrastructure. Using i-Tree Canopy, the analysis focuses on five urban green spaces covering more than 30 hectares, considering the tree ratio relative to the total area. The evaluation encompasses aspects of thermal environment, aquatic environment, and atmospheric environment to assess the overall eco-friendliness in terms of climate change adaptation. The results indicate that an increase in the tree ratio correlates with improved eco-friendliness in terms of thermal, aquatic, and atmospheric environments. In particular, it is necessary to prioritize consideration of the water environment sector in order to realize climate change adaptive green infrastructure, such as increasing green space in urban areas, as it has been confirmed that four out of five target sites are specialized in improving the water environment.

It is not an exaggeration to say that a single fire causes a lot of damage, so fires are one of the disaster situations that must be alerted as soon as possible. Various technologies have been utilized so far because preventing and detecting fires can never be completely accomplished with individual human efforts. Recently, deep learning technology has been developed, and fire detection systems using object detection neural networks are being actively studied. In this paper, we propose a new fire detection system that improves the previously studied fire detection system. We train the YOLOv8 model using refined datasets through improved labeling methods, derive results, and demonstrate the superiority of the proposed system by comparing it with the results of previous studies.

In the present modern day, as the complexity and size of SoC(System on Chip) increase, the importance of design verification are increasing, Therefore it takes a lot of time to verify the design. There is an emerging need to manage the verification environment faster and more efficiently by reusing the existing verification environment. UVM-based verification is a standardized and highly reliable verification method widely adopted and used in the semiconductor industry. This paper presents a UVM-based verification for the 4 tap equalizer module with a systolic array structure. Through the constraints randomization, it was confirmed that various test scenarios stimulus were generated. In addition, by verifying a simulation comparing the actual DUT outputs with the MATLAB reference outputs, the reuse and efficiency of the UVM test bench could be confirmed.

The purpose of this study is to identify the factors that impact the user's intention to accept technology when Introducing new information security systems for the workers of a semiconductor company. The findings of this study were as follows. First, the factors of a company's information security systems, namely reliability, expertise, availability, security, and economic efficiency, all significantly and positively impacted performance expectations. Second, the performance expectation of introducing information security systems for a company significantly and positively impacted the intention to accept technology. Third, the social impact of introducing information security systems for a company had a significant and positive impact on technology acceptance intention. Fourth, the facilitating conditions for introducing a company's information security systems significantly and positively impacted technology acceptance intention. Fifth, as for the moderating effect of innovation resistance, the moderating effect was significant in the paths of [performance expectation -> technology acceptance intention], [social impact -> technology acceptance intention], and [facilitating conditions -> technology acceptance intention]. The implication of this study is that the factors to be considered when introducing information security systems were provided to companies that are the actors of their proliferation, providing the base data to lay the foundation for introducing security technologies and their proliferation.

Recently, high-performance BLDC(Brushless DC) motors are being applied to various fields such as industrial and personal mobility devices and drones. To achieve the best performance of BLDC, sensors such as hall sensors, encoders, and resolvers are used to determine the position of the rotor, and various speed control technologies are being developed. However, due to problems with high-speed control due to external environmental factors and frequency bandwidth of semiconductor sensing devices, research on BLDC motors without semiconductor sensing devices is in progress. Therefore, in this study, a signal brush was added to the end of the rotor of a BLDC motor and the rotor position of the BLDC motor was detected by analyzing the signal output through the signal brush.

In this paper, a state change study was conducted through Frequency Domain Reflectometry (FDR) technology for the process chamber of plasma equipment for semiconductor manufacturing. In the experiment, by direct connecting the network analyzer to the RF matcher input of the 300 mm plasma enhanced chemical vapor deposition (PECVD) chamber, S11 was measured in a situation where plasma was not applied, and the frequency domain reacting to the chamber state change was searched. Response factors to changes in the status, such as temperature, spacing of the heating chuck, internal pressure difference, and process gas supply state were confirmed. Through this, the frequency domain in which a change in the reflection value was detected through repeated experiments. The reliability of the measured micro-displacement was verified through reproducibility experiments.

In this paper, we propose a SIFT feature-based dynamic stitching algorithm for image calibration and correction of a 360-degree surround view system. The existing surround view system requires a lot of processing time and money because in the process of image calibration and correction. The traditional marker patterns are placed around the vehicle and correction is performed manually. Therefore, in this study, images captured with four fisheye cameras mounted on the surround view system were distorted and then matched with the same feature points in adjacent images through SIFT-based feature point extraction to enable image stitching without a fixed marker pattern.

As research on autonomous driving technology becomes more active, various studies on signal recognition of traffic lights are also being conducted. When recognizing traffic lights with different purposes and shapes, such as pedestrian traffic lights, vehicle-only traffic lights, and right-turn traffic lights, existing classification methods may cause misrecognition problems. Therefore, in this study, we studied a model that allows accurate signal recognition by subdividing the classification of signals according to the purpose and type of traffic lights. A signal recognition model was created by classifying traffic lights according to their shape and purpose into horizontal, vertical, right turn, etc., and by comparing them with the existing signal recognition model based on YOLOv5, it was confirmed that more correct and accurate recognition was possible.

Direct cache-to-cache transfer has been studied to reduce the latency and bandwidth consumption related to the shared data in multiprocessor system. Even though these studies lead to meaningful results, they assume that caches consist of SRAM. For example, if the system employs the non-volatile memory, the one of the most important parts to consider is to decrease the number of write operations. This paper proposes a hybrid write avoidance cache coherence protocol that considers the hybrid cache architecture. A new state is added to finely control what is stored in the non-volatile memory area, and experimental results showed that the number of writes was reduced by about 36% compared to the existing schemes.

In indoor environments, since global positioning system (GPS) signals can be blocked by obstacles, such as building structure. the performance of GPS-based positioning methods can be degraded because of the loss of GPS signals. To solve this problem, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope, accelerometer, and magnetometer, have been proposed to enhance the positioning accuracy in indoor environments. IMU-based positioning methods can estimate the location of the user by calculating the velocity and heading angle of the user without the help of GPS. However, low-cost MEMS IMUs may lead to drift error and large bias. In addition, positioning errors in IMU-based positioning approaches can be caused by the irrelevant motion of the pedestrian. In this study, we propose an enhanced indoor positioning method that provides more reliable localization results by using the camera, light detection and right (LiDAR), and ARKit framework on the iPhone. Through reliable positioning results and augmented reality (AR) experiences, our indoor positioning system can provide indoor space guidance services.

Digital Twin technology is rapidly transforming various industries by providing comprehensive virtual models that replicate physical objects or processes. In the context of agriculture, digital twin can be a game-changer. This technology can help in creating precise simulations of farming scenarios, thereby enabling farmers to make data-driven decisions and optimize farm operations. The potential benefits include improved crop yields, resource efficiency, and environmental sustainability. However, the implementation of digital twin technology in agriculture poses challenges, such as data management issues and the need for robust IoT infrastructure. Despite these hurdles, the future of digital twin in agriculture looks promising, with ongoing research and developments aimed at overcoming these obstacles.

A water sterilization system is developed utilizing a 275 nm-wavelength LED light source equipped with a TIR lens. The system's light source is constructed by combining a 275 nm-wavelength UVC LED, known for its germicidal properties, with a TIR lens having a direction angle of 6.8 degrees. The optical simulation software 'LightTools' is employed to design and optimize the intensity of deep ultraviolet sterilizing light irradiation, its distribution, and sterilization capacity. In the inactivation experiment with E. coli, the water sterilizer system achieved a sterilization rate of 78.92 % while maintaining a water flow capacity of 50 L/min. Compared to the conventional mercury lamp light source water sterilizer system, the UVC LED water sterilizer system addresses environmental concerns related to mercury usage and offers advantages in terms of lifespan and durability.

The europium doped yttrium oxide (Y₂O₃:Eu³⁺) thin film was formed on a Si substrate by the conventional spin-coating process followed by rapid thermal annealing (RTA) treatment. The spinning profiles such as rotation speed, acceleration and holding times were controlled during the spin-coating process for the best condition of the Y₂O₃:Eu³⁺ thin film. The RTA treatment was conducted for several temperature in order to crystallize the spin coated film. The Y₂O₃:Eu³⁺ thin film presented best performance in the conditions of 4000 rpm, 30 s and 10 s of rotation speed, acceleration time and holding time, respectively, at a fixed RTA temperature of 900 ℃.

The application of the technology for forming Al₂O₃ thin films using ALD(atomic layer deposition) method is rapidly increasing in the semiconductor and display fields. In order to increase the efficiency of the ALD process in a mass production line, metallic by-products generated from the ALD process chamber must be effectively collected. By collecting by-products flowing out of the chamber with a cold trap device before they go to the vacuum pump, damage to the vacuum pump can be prevented and the work room can be maintained stably, resulting in increased process flow rate. In this study, a cold trap was installed between the ALD process chamber and the dry pump to measure and analyze by-products generated during the Al₂O₃ thin film deposition process. As a result, it was confirmed that Al and O elements were discharged, and the collection forms were two types: bulk and powder. And the binding energy peaked at 73.7 ~ 74.3 eV, the binding energy of Al 2p, and 530.7 eV, the binding energy of O 1s, indicating that the binding structure was Al-O.

In this paper, the difference between the S-parameter and the characteristic impedance according to the structural change of the fine pitch coaxial socket was analyzed. A pitch of the probe pin was applied to 0.20mm, and ground pins of different conditions were placed on each of the five signal pins. Insertion loss and reflection loss were analyzed for the coaxial socket of normal structure and the two sockets of the proposed structure. In addition, the difference in characteristic impedance was analyzed using time domain reflectometry. Through the analysis, it was confirmed that the characteristic impedance was improved applying the new structures of the socket at the same pitch

In this paper, we propose structures of a socket that can improve signal transmission characteristics compared to the existing silicone rubber socket. A coaxial rubber socket was proposed to improve signal distortion due to electromagnetic interference between signal lines. And an air gap rubber socket was proposed to improve the degradation of reflection characteristics due to high dielectric constant. Using a 3D electromagnetic field simulator, the S-parameter and crosstalk of the three sockets were compared and the signal transmission characteristics were analyzed. In both coaxial rubber socket and the air gap rubber socket, S-parameter and crosstalk were improved compared to the silicone rubber socket. Among them, the air gap rubber socket was the best for S-parameter, and the coaxial rubber socket was the best for crosstalk.

Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.