Silicon carbide (SiC) has emerged as a promising material for next-generation power semiconductor materials, due to its high thermal conductivity and high critical electric field (~3 MV/cm) with a wide bandgap of 3.3 eV. This permits SiC devices to operate at lower on-resistance and higher breakdown voltage. However, to improve device performance, advanced research is still needed to reduce point defects in the SiC epitaxial layer. This work investigated the electrical characteristics and defect properties using DLTS analysis. Four deep level defects generated by the implantation process and during epitaxial layer growth were detected. Trap parameters such as energy level, capture-cross section, trap density were obtained from an Arrhenius plot. To investigate the impact of defects on the device, a 2D TCAD simulation was conducted using the same device structure, and the extracted defect parameters were added to confirm electrical characteristics. The degradation of device performance such as an increase in on-resistance by adding trap parameters was confirmed.

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

The need for lightweight yet strong materials is being demanded in all industries. Carbon fiber-reinforced plastic is a material with increased strength by attaching carbon fiber to plastic, and is widely used in the aerospace industry, ships, automobiles, and civil engineering based on its low density. Carbon-reinforced fiber plastic is a material widely used in parts and manufactured products, and structural analysis simulation is required during design, and application of actual material properties is necessary for accurate structural analysis simulation. In the case of carbon-reinforced fiber plastics, it is reported that there is a porosity of around 0.5% to 6%, and it is necessary to check the change in material properties according to the porosity and pore shape. It was confirmed by applying the method. It was confirmed that the change in elastic modulus according to the porosity was 10.7% different from the base material when the porosity was 6.0%, and the Poisson's ratio was confirmed to be less than 3.0%. It was confirmed that the elliptical spherical pore derived different material properties from the spherical pore depending on the pore shape, and it was confirmed that the shape of the pore had to be confirmed to derive equivalent material properties.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

Zinc-ion Batteries (ZIBs) are currently considered to be effective energy storage devices for wearable electronics because of their low cost and high safety. Indeed, ZIBs show high power density and safety compared with conventional lithium ion batteries (LIBs) and exhibit high energy density in comparison with supercapacitors (SCs). However, in spite of their advantages, further current collector development is needed to enhance the electrochemical performance of ZIBs. To design the optimized current collector for high performance ZIBs, a high quality graphene film is suggested here, with improved electrical conductivity by controlling the defects in the graphene film. The graphene film showed improved electrical conductivity and good electron transfer between the current collector and active material, which led to a high specific capacity of 346.3 mAh g-1 at a current density of 100 mA g-1, a high-rate performance with 116.3 mAh g-1 at a current density of 2,000 mA g-1, and good cycling stability (68.0 % after 100 cycles at a current density of 1,000 mA g-1). The improved electrochemical performance is firmly because of the defects-controlled graphene film, leading to improved electrical conductivity and thus more efficient electron transfer between the current collector and active material.

In this study, the safety aspects were studied by comparing the charge control characteristics of the two vehicles when a failure occurs between the OBC including the charging port or the charging door module (CDM) during slow charging using the In Cable Control Box (ICCB) for a long time.When the AC terminal was momentarily disconnected during charging, the Model-3 vehicle was charged normally if the AC circuit was disconnected up to three times, and the charging control was stopped when the number of disconnects reached four times. However, in the Ioniq-5 vehicle, charging control was normally performed when the disconnected AC circuit was normally connected regardless of the number of disconnection.

In this paper, a study was conducted on the analysis of communication circuit faults using oscilloscope waveform analysis. Circuit resistance was calculated based on voltage and operating current values using a simple equation, and it was confirmed that the increase in resistance of the communication circuit could be analyzed by analyzing the voltage level during transmitter operation. By combining information of the controller ID, the location of the fault was identified and it was concluded that the location of the fault can be quickly found by analyzing the oscilloscope waveform and the controller ID information. Additionally, the value of communication line contact resistance can be calculated using a simple equation, and the location of the fault can be found by analyzing the communication voltage level and ID information.

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users’ safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.

강풍, 폭우 등 이상기후의 대형화와 빈도 증가로 인해 나무가 부러지거나 쓰러지는 훼손이 증가하고 있으나 나무 내부의 공동, 부후 등 구조적 결함은 육안조사로 판별이 어렵기 때문에 예측을 통한 사전대응에 한계가 있다. 비파괴 음파단층촬영은 나무에 미치는 물리적 훼손을 최소화하면서 내부결함을 추정하는 방법으로 내부결함 진단에 효율적이 나 수종별 정확도에 차이가 발생하기 때문에 현장적용 전 측정결과의 신뢰성 분석이 선행되어야 한다. 이번 연구는 우리나라 대표 수종인 소나무와 은행나무 노거수를 대상으로 음파단층촬영의 신뢰성 검증을 위해 침입성 드릴저항 측정을 교차 적용하여 목재 내부결함을 측정하고 평가결과를 비교하였다. 두 집단 간 결함부 측정 평균값에 대한 t검정 결과 소나무는 통계적으로 유의한 차이가 없는 반면, 은행나무는 유의성에 차이가 있었다. 선형회귀분석 결과 두 수종 모두 드릴저항그래프의 결함이 증가할 때 음파단층영상 결함이 증가하는 양의 상관관계를 보였다.

In this study, surface roughness and interfacial defect characteristics were analyzed after forming a high-k oxide film on the surface of a prime wafer and a test wafer, to study the possibility of improving the quality of the test wafer. As a result of checking the roughness, the deviation in the test after raising the oxide film was 0.1 nm, which was twice as large as that of the Prime. As a result of current-voltage analysis, Prime after PMA was 1.07 × 10 A/cm2 and Test was 5.61 × 10 A/cm2, which was about 5 times lower than Prime. As a result of analyzing the defects inside the oxide film using the capacitancevoltage characteristic, before PMA Prime showed a higher electrical defect of 0.85 × 1012 cm2 in slow state density and 0.41 × 1013 cm2 in fixed oxide charge. However, after PMA, it was confirmed that Prime had a lower defect of 4.79 × 1011 cm2 in slow state density and 1.33 × 1012 cm2 in fixed oxide charge. The above results confirm the difference in surface roughness and defects between the Test and Prime wafer.

This research studied faults that may occur during slow charging using the J1772 adapter of Tesla Model-3 electric vehicles. When the AC terminal was instantaneously disconnected, charging was performed normally when an AC circuit with disconnection up to three disconnection times was connected. Charging control was suspended when the number of disconnection reached four times. However, if the AC disconnection time exceeded 22 seconds, the charging control was stopped regardless of the number of disconnection. When a contact failure occurred at the AC terminal, high surge current and noise occurred. However, when the contact improved, the charging control continued. In terms of safety, it seems necessary to take measures such as stopping charging control when detecting noise.

In the event of an defective wire in the low-speed CAN communication of vehicle, the problem had to be solved by relying on fault codes or using expensive measuring equipment. An experiment was conducted to analyze waveforms of communication circuits with wire conditions such as normal, short circuits in the main body, and mutual short circuits. When the controller drives the CAN transceiver and transmits a message, the voltage and current waveforms were measured using an OEM oscilloscope to check for abnormalities in the circuit. As a result, it was confirmed that when a defective wire occurs in low-speed CAN communication, the CAN driver can switch to the fail-safe mode to exchange normal messages.

PURPOSES : The surface distress of asphalt pavements is one of the major factors affecting the safety of road users. The aim of this study was to analyze the factors influencing the occurrence of surface distress and statistically predict its annual change to contribute to more reasonable asphalt pavement management using the data periodically collected by the national highway pavement data management system. METHODS : In this study, the factors that were expected to influence the surface distress were determined by reviewing the literature. The normality was secured by changing the forms of the variables to make the distribution of the variables got closer to normal distribution. In addition, min-max normalization was performed to minimize the effect of the unit and magnitude of the candidate independent variables on the dependent variable. The final candidate independent variables were determined by analyzing the correlation between the annual surface distress change and each candidate independent variable. In addition, a prediction model was developed by performing data grouping and multi-regression analysis. RESULTS : An annual surface distress change prediction model was developed using present surface distress, age, and below 0 ℃ days as the independent variables. As a result of sensitivity analysis, the surface distress affected the annual surface distress change the most. The positive correlation between the dependent variable and each independent variable demonstrated engineering and statistical meaningfulness of the prediction model. CONCLUSIONS : The surface distress in the future can be predicted by applying the annual surface distress prediction model to the national highway asphalt pavement sections with survey data. In addition, the prediction model can be applied to the national highway pavement condition index (NHPCI) evaluating the national highway asphalt pavement conditions to be used in the prediction of future NHPCI.

초음파 탐상은 다양한 콘크리트 구조물의 비파괴검사에서 활용된다. 본 연구에서는 골재형상을 고려한 골재-모르타르 모델 생성과 초음파 전파 해석을 수행하였다. 실제 골재형상을 반영하기 위해 이미지처리를 통한 골재-모르타르 단면으로부터 모르타르와 골재 영역을 파악하고, 영역 경계형상을 보존하면서 격자를 생성하는 기법을 개발하였다. 개발된 기법에서는 모든 격자가 4각형으로 생성된다. 골재-모르타르 모델을 통해 초음파 전파 해석을 수행하였고 모델을 반무한체로 간주하기 위해 CALM 기반 경계흡수 조건을 적용하였다. 골재 및 결함을 포함한 이미지로부터 격자를 생성한 뒤, 결함 영역에 포함된 격자를 제거하여 공극결함을 모사하였다. 본격적인 결함탐지 전 선행 해석을 통해 모델 동특성을 고려한 적절한 가진 주파수를 결정 및 가진 신호형상을 설계하였다. 이후 case 별 초음파 전파 해석을 통해 신호를 획득하고 신호 에너지 맵핑 작업을 통해 내부 결함을 가시화 하였다. 가시화 결과, 골재에 의한 다수 반사 및 산란현상이 관찰되지만 결함부에서 신호 에너지는 가장 높게 나타났으며 모든 해석 case에서 결함위치 추정이 가능하였다. 또한 균열의 경우 형상파악도 가능하였다.

자연재해 발생을 예방하기 위한 방재센서 기술이 중요하며 광섬유를 이용한 센서에 대한 관심이 높아지고 있다. 본 논문은 광섬유 센서 내장 탄소섬유시트로 보강된 RC보의 계측된 데이터로 결함 탐지 연구를 수행하였다. 미분의 국부적 변동 특성을 이용한 Method Ⅰ과 컨벌루션 방법을 이용한 Method Ⅱ를 비교, 분석하였다. 다른 차원의 데이터를 비교하기 위해서 무차원화 시켰으며, 분석 결과 Mehtod Ⅱ가 결함의 위치를 예리하게 잘 탐지하는 것으로 나타났다. Method Ⅱ인 컨벌루션에 사용 되는 필터 벡터를 잘 응용하면 더 좋은 효과를 기대할 수 있을 것으로 판단된다.

Defects in most structures can be generated not only on outside but also on inside or on the back-side during the manufacturing or construction process. Also they cause the growth of defects due to operation of various complex environments and structures will be destroyed eventually. In order to improve the reliability of the structure, the detection and size-estimation of defects should be investigated. In this paper, as an extension of previous studies on surface defects, two-dimensional artificial backside cracks (blind cracks) into paramagnetic material were evaluated by using the same aluminum probe. The potential drop at the defect position is distributed in the n-shape in the case of the back defect, which is different from results of the surface defect (u-shape). The potential drops at the defect position are measured with the largest value. The potential drop ratio (Vcmax/Vs) for the defective position is used as a parameter to predict the thickness (l) of defect position.