Most of the production process is performed in a cleanroom in the case of facilities that produce semiconductor chips or display panels. Therefore, environmental management of cleanrooms is very important for product yield and quality control. Among them, airborne particles are a representative management item enough to be the standard for the actual cleanroom rating, and it is a part of the Fab or Facility monitoring system, and the sequential particle monitoring system is mainly used. However, this method has a problem in that measurement efficiency decreases as the length of the sampling tube increases. In addition, a statistically significant test of deterioration in efficiency has rarely been performed. Therefore, in this study, the statistically significant test between the number of particles measured by InSitu and the number of particles measured for each sampling tube ends(Remote). Through this, the efficiency degradation problem of the sequential particle monitoring system was confirmed by a statistical method.

In this paper, we proposed a software framework structure to apply ORB-SLAM, the most representative of SLAM algorithms, so that map creation and location estimation technology can be applied through tethered AR glasses. Since tethered AR glasses perform only the role of an input/output device, the processing of camera and sensor data and the generation of images to be displayed through the optical display module must be performed through the host. At this time, an Android-based mobile device is adopted as the host. Therefore, the major libraries required for the implementation of AR contents for AR glasses were hierarchically organized, and spatial recognition and location estimation functions using SLAM were verified.

This paper proposes a lightweight deep learning network for detecting an image splicing forgery. The research on image forgery detection using CNN, a deep learning network, and research on detecting and localizing forgery in pixel units are in progress. Among them, CAT-Net, which learns the discrete cosine transform coefficients of images together with images, was released in 2022. The DCT coefficients presented by CAT-Net are combined with the JPEG artifact learning module and the backbone model as pre-learning, and the weights are fixed. The dataset used for pre-training is not included in the public dataset, and the backbone model has a relatively large number of network parameters, which causes overfitting in a small dataset, hindering generalization performance. In this paper, this learning module is designed to learn the characterization depending on the DCT domain in real-time during network training without pre-training. The DCT RRU-Net proposed in this paper is a network that combines RRU-Net which detects forgery by learning only images and JPEG artifact learning module. It is confirmed that the network parameters are less than those of CAT-Net, the detection performance of forgery is better than that of RRU-Net, and the generalization performance for various datasets improves through the network architecture and training method of DCT RRU-Net.

We have investigated the effect of the concentration of oxygen vacancies on the characteristics of Mo-doped ZnO (MZO) thin films for the TCO (transparent conducting oxide). For this purpose, MZO thin films were deposited by RF magnetron sputtering at different substrate temperature from room temperature to 300℃. The electrical resistivity of the MZO films decreases with increasing substrate temperature up to 100℃ and then gradually increases at higher temperatures. To investigate the influences of the ambient gases, the flow rate of oxygen and hydrogen in argon was varied from 0.1 sccm to 0.5 sccm. The MZO thin films were preferentially oriented to the (002) direction, regardless of the ambient gases used. The electrical resistivity of the MZO thin films increased with increasing O₂ flow rates, whereas the electrical resistivity decreased sharply under an Ar+H₂ atmosphere and was nearly the same, regardless of the H₂ flow rate used. As the oxygen vacancy concentration increases, the resistivity intended to decrease. In conclusion, Oxygen vacancy affects the MZO thin film's electrical characteristics. All the films showed an average transmittance of over 80% in the visible range.

Organic light-emitting diode(OLED) is very thin organic films which are hundreds of nanometers. Unlike bottom-emission OLED(BEOLED), top-emission OLED(TEOLED) emits light out the front, opaque moisture absorbents or metal foils can't be used to prevent moisture and oxygen. And it is difficult to have flexible characteristics with glass encapsulation, so thin film encapsulation which can compensate for those two disadvantages is mainly used. In this study, Al₂O₃ thin films by atomic layer deposition(ALD) were examined by changing the argon gas purge flow rate and we applied this Al₂O₃ thin films to the encapsulation of TEOLED. Ag / ITO / N,N'-Di-[(1-naphthyl)-N,N'-diphenyl]-1,1'-biphenyl-4,4'-diamine / tris-(8-hydroxyquinoline) aluminum/ LiF / Mg:Ag (1:9) were used to fabricate OLED device. The characteristics such as brightness, current density, and power efficiency are compared. And it was confirmed that with a thickness of 40 nm Al₂O₃ thin film encapsulation process did not affect OLED properties. And it was enough to maintain a proper OLED operation for about 9 hours.

Recently, the use of stable lithium nanostructures as substrates and electrodes for secondary batteries can be a fundamental alternative to the development of next-generation system semiconductor devices. However, lithium structures pose safety concerns by severely limiting battery life due to the growth of Li dendrites during rapid charge/discharge cycles. Also, enabling long cyclability of high-voltage oxide cathodes is a persistent challenge for all-solid-state batteries, largely because of their poor interfacial stabilities against oxide solid electrolytes. For the development of next-generation system semiconductor devices, solid electrolyte nanostructures, which are used in high-density micro-energy storage devices and avoid the instability of liquid electrolytes, can be promising alternatives for next-generation batteries. Nevertheless, poor lithium ion conductivity and structural defects at room temperature have been pointed out as limitations. In this study, a low-dimensional Graphene Oxide (GO) structure was applied to demonstrate stable operation characteristics based on Li+ ion conductivity and excellent electrochemical performance. The low-dimensional structure of GO-based solid electrolytes can provide an important strategy for stable scalable solid-state power system semiconductor applications at room temperature. The device using uncoated bare NCA delivers a low capacity of 89 mA h g-1, while the cell using GO-coated NCA delivers a high capacity of 158 mA h g−1 and a low polarization. A full Li GO-based device was fabricated to demonstrate the practicality of the modified Li structure using the Li-GO heterointerface. This study promises that the lowdimensional structure of Li-GO can be an effective approach for the stabilization of solid-state power system semiconductor architectures.

As the demand for large flexible displays such as tablet computers continues to rise, there is an increasing need for cost-efficient colorless and optically transparent polyimide film that can meet the desired performance, particularly optical transmittance. In this study, we investigated a detailed procedure for achieving optimal optical transmittance using two different combinations of monomers: 6FDA+BAPB and 6FDA+BPA+TFDB. We employed a design of experiment method to systematically synthesize polymers, allowing for the optimization of optical transmittance. In addition, we were able to achieve uniform thickness in the films by using a doctor blade. By comparing the price and optical transmittance of four different monomer combinations, we obtained fundamental data on the production of polyimide films that can be customized to meet the specific price and performance requirements of manufacturers. This approach enables users to select the most suitable polyimide film based on their desired price and performance parameters while achieving optimal optical transmittance.

Recently, with the explosive increase of video streaming services, real-time live broadcasting has also increased, which leads to an infringement problem for user privacy. So, to solve such problems, we proposed the real image blurring system using dlib face-recognition library. 68 face landmarks are extracted and convert into 128 vector values. After that the proposed system tries to compare this value with the image in the database, and if it is over 0.45, it is considered as different person and image blurring processing is performed. With the proposed system, it is possible to solve the problem of user privacy infringement, and also to be utilized to detect the specific person. Through experimental results, the proposed system has an accuracy of more than 90% in terms of face recognition.

This paper discusses the design of 32Mbyte asynchronous nonvolatile memory modules, which includes self-diagnosis and RTC (Real Time Clock) functions to enhance their data stability and reliability. Nonvolatile memory modules can maintain data even in a power-off state, thereby improving the stability and reliability of a system or device. However, due to the possibility of data error due to electrical or physical reasons, additional data loss prevention methods are required. To minimize data error in asynchronous nonvolatile memory modules, this paper proposes the use of voltage monitoring circuits, self-diagnosis, BBT (Bad Block Table), ECC (Error Correction Code), CRC (Cyclic Redundancy Check)32, and data check sum, data recording method using RTC. Prototypes have been produced to confirm correct operation and suggest the possibility of commercialization.

In this paper, we propose an antirandom pattern-based test method considering power consumption to compensate for the problem that the fault coverage through random test decreases or the test time increases significantly when the DUT circuit structure is complex or large. In the proposed method, a group unit test pattern generation process and rearrangement process are added to improve the problems of long calculation time and high-power consumption, which are disadvantages of the previous antirandom test.

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

Various devices have been emerging as a means of measuring indoor air quality, and among them, there are devices that support real-time remote monitoring through IoT technology and a cloud environment. To improve indoor air quality, based on the results determined by measuring devices, air purifiers or ventilation systems may need to be operated, and temperature and humidity control may be required. In this paper, we propose a design of indoor air quality measuring devices required for indoor air quality evaluation, and of the system needed to control relevant devices to improve indoor air quality through the interaction with the measuring devices. Currently, the servers for the interaction of indoor air quality devices and IoT devices are divided into conventional server type and serverless type, comparing the differences in response time of IoT devices to changes of indoor air quality.

In this study, we investigated the effects of BiAlSiO glass composition on its glass forming range, thermal properties, and plasma resistance. The results showed that increasing the Al2O3 content suppressed the tendency for crystallization and hindered glass formation beyond a certain threshold. Bi2O3 was found to increase the content of non-bridging oxygen, resulting in a decrease in glass transition temperature and an increase in thermal expansion coefficient. Furthermore, the etching rate was found to improve with increasing Al2O3 content but decrease with increasing SiO2 content. It was concluded that the boiling point of fluorinated compounds should be considered to 900℃. Therefore, this study is expected to contribute to the understanding of the properties of BiAlSiO glass and its application to low temperature melting PRG compositions.

In many manufacturing settings, including the semiconductor industry, products are completed by producing and assembling various components. Sorting out from randomly mixed parts and classification operations takes a lot of time and labor. Recently, many efforts have been made to select and assemble correct parts from mixed parts using robots. Automating the sorting and classification of randomly mixed components is difficult since various objects and the positions and attitudes of robots and cameras in 3D space need to be known. Previously, only objects in specific positions were grasped by robots or people sorting items directly. To enable robots to pick up random objects in 3D space, bin picking technology is required. To realize bin picking technology, it is essential to understand the coordinate system information between the robot, the grasping target object, and the camera. Calibration work to understand the coordinate system information between them is necessary to grasp the object recognized by the camera. It is difficult to restore the depth value of 2D images when 3D restoration is performed, which is necessary for bin picking technology. In this paper, we propose to use depth information of RGB-D camera for Z value in rotation and movement conversion used in calibration. Proceed with camera calibration for accurate coordinate system conversion of objects in 2D images, and proceed with calibration of robot and camera. We proved the effectiveness of the proposed method through accuracy evaluations for camera calibration and calibration between robots and cameras.

Metal oxide semiconductor (MOS) adapting spray-pyrolysis deposition technique has drawn large attention based on their high quality of intrinsic and electrical properties in addition to simple and low-cost processibility. To fully utilize the merits of MOS field-effect transistors (FETs) , transparency, it is important to understand the instability behaviors of FETs under illumination. Here, we studied the photo-induced properties of nc-ZnO/ZnO field-effect transistors (FETs) based on spray-pyrolysis under illumination which incorporating ZnO nanocrystalline nanoparticles into typical ZnO precursor. Our experiments reveal that nc-ZnO in active layer suppressed the light instabilities of FETs.

Y₂SiO₅ Powder based on silicon and yttrium is well known as powder phosphors due to their excellent sustainability and efficiency. A new electroluminescence device was fabricated with Y₂SiO₅:Eu³⁺ powder phosphors though a simple screen printing method. The powder-dispersed electroluminescence device consisted of the Y₂SiO₅:Eu³⁺ powder-dispersed phosphor layer and BaTiO₃-dispersed dielectric layer. The annealing temperature of the phosphor for the best powder electroluminescence performance was optimized to high temperature in ambient atmosphere though a solid-state reaction. The Eu³⁺ concentration for the best device performance was also investigated and furthermore, the thermal dependence of the electroluminescence intensity was investigated at the operating voltage at 100℃, which is the Curie temperature of the BaTiO₃ layer. And the intensity was exponentially increased with voltage and increased linearly with frequency.

In software engineering, requirement analysis is an important task throughout the process and takes up a high proportion. However, factors that fail to analyze requirements include communication failure, different understanding of the meaning of requirements, and failure to perform requirements normally. To solve this problem, we derived actors and behaviors using morpheme analysis and BERT algorithms in the Korean requirement document and constructed them as ontologies. A chatbot system with ontology data is constructed to derive a final system event list through Q&A with users. The chatbot system generates the derived system event list as a requirement diagram and a requirement specification and provides it to the user. Through the above system, diagrams and specifications with a level of coverage complied with Korean requirement documents were created.

In the case of high-speed data transmission on high multilayer boards, signal coherence is a problem, especially due to the via hole, and a solution to improve return loss or insertion loss by applying a back drill to the via hole is being proposed. In this paper, Near-Field Electromagnetic measurements were made on a high multilayer board to determine how the presence or absence of back drill affects signal consistency. For this purpose, we used a signal generator, spectrum analyzer, and EMC scanner on a test board to determine if it is possible to distinguish between areas with and without back drill in the via holes of the stubs on the board. Also, we analyzed the measured value of S11, S21 and EMC etc. for how much it improves the signal attenuation of the stub with back drill. Through this, we knew that less electromagnetic waves are generated the stub via with back drill. At future research, we will analyze how much it improves the signal loss and electromagnetic waves due to the depth of back drill.

NFT technology is mostly used to create digital drawings, characters, and items, and to simply buy and sell, but research and development to spread to various contents of NFT are somewhat marginal. Therefore, this study aims to solve the above-described problems. Depending on the exercise performance, it allows users to create and trade custom NFTs. In addition, it supports users to own customized digital works through exercise performance or to make money by trading them. Through it, the aim is to enhance users' positive interest in exercise and provide devices and methods for providing customized NFT creation and trading services that can help them develop exercise habits.

Agriculture plays an industrial and economic role, as well as an environmental and ecological conservation role, group harmony and the inheritance of traditional culture. However, no matter how advanced the industry is, the basic food necessary for human life can only be produced through the photosynthesis of plants with natural resources such as the sun, water, and air. The Food and Agriculture Organization of the United Nations (FAO) predicts that the world's population will increase by another 2 billion people by 2050, and it faces a myriad of complex and diverse factors to consider, including climate change, food security concerns, and global ecosystems and political factors. In particular, in order to solve problems such as increasing productivity and production of agricultural products, improving quality, and saving energy, it is difficult to solve them with traditional farming methods. Recently, with the wind of the 4th industrial revolution, ICT convergence technology and artificial intelligence have been rapidly developing in many fields, but it is also true that the application of new technologies is somewhat delayed due to the unique characteristics of agriculture. However, in recent years, as ICT and artificial intelligence utilization technologies have been developed and applied by many researchers, a revolution is also taking place in agriculture. This paper summarizes the current state of research so far in four categories of agriculture, namely crop cultivation environment management, soil management, pest management, and irrigation management, and smart farm research data that has recently been actively developed around the world.

As sputter equipment becomes more complex, it becomes increasingly difficult to understand the parameters that affect the thickness uniformity of thin metal film deposited by sputter. To address this issue, we verified a deep learning model that can predict complex relationships. Specifically, we trained the model to predict the height of 36 magnets based on the thickness of the material, using Support Vector Machine (SVM), Multilayer Perceptron (MLP), 1D-Convolutional Neural Network (1D-CNN), and 2D-Convolutional Neural Network (2D-CNN) algorithms. After evaluating each model, we found that the MLP model exhibited the best performance, especially when the dataset was constructed regardless of the thin film material. In conclusion, our study suggests that it is possible to predict the sputter equipment source using film thickness data through a deep learning model, which makes it easier to understand the relationship between film thickness and sputter equipment.

Partially magnetized capacitively coupled plasma (CCP) sources are investigated using a two-dimensional kinetic simulation code named EDIPIC-2D. A converging numerical solution was obtained for CCP with a 60 MHz power source, while properly capturing the dynamics of electrons and power absorption over a single RF period. The effects of magnetic fields with different orientations were evaluated. Axial magnetic fields caused changes in the spatial distribution of plasma density, affecting the loss channel. Transverse magnetic fields enhanced stochastic heating near the powered electrode, leading to an increase in plasma density while the significant E×B drift loss compensated for this rise.

반도체 양산을 진행하며 얻어지는 여러 공정 데이터들로 사용 전압을 예측하여 에너지 효율적인 제품을 위한 목적으로 연구를 시작했다. 각각의 feature들 단독으로 전압을 예측하기 어려웠던 문제를 머신 러닝을 통해, 특히 Ensemble model을 이용함으로써 단일 모델보다 정확한 예측을 할 수 있었다. 더욱 중요한 시사점으로는 feature importance 분석을 통해 모델 예측에 영향이 큰 feature와 작은 feature에 대한 분석이다. 영향도가 높은 feature를 통해 비슷한 계열의 측정값을 늘리고, 낮은 feature 들의 문제점을 개선함으로써 차세대 제품에서 더욱 정확도 높은 모델을 위한 발판을 마련할 수 있었다.

Image compression is a fundamental technique in the field of digital image processing, which will help to decrease the storage space and to transmit the files efficiently. Recently many deep learning techniques have been proposed to promise results on image compression field. Since many image compression techniques have artifact problems, this paper has compared two deep learning approaches to verify their performance experimentally to solve the problems. One of the approaches is a deep autoencoder technique, and another is a deep convolutional neural network (CNN). For those results in the performance of peak signal-to-noise and root mean square error, this paper shows that deep autoencoder method has more advantages than deep CNN approach.

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.