Recently, in the semiconductor industry, memory device market is focusing on producing ultra-thin wafers for high integration. In the wafer manufacturing process, wafers after backgrinding and CMP process must be picked up as individual dies and subjected to be peeled off from the dicing tape. However, ultra-thin dies are vulnerable to the possibility of breakage and failure in their thickness and size. This research studies the mechanism of peeling a die with a high-aspect ratio using a thermopneumatic method instead of a die ejector with physical pins. Setting compressed air and the temperature as main factors, we determine the success of the digester using thermopneumatic system and analyze the good die to find the possibility of making mass-production equipment.

High bandwidth memory a core technology of the future memory semiconductor industry, is attracting attention. Temporary bonding and debonding process technology, which plays an important role in high bandwidth memory process technology, is also being studied. In this process, total thickness variation is a major factor determining wafer performance. In this study, the reliability of the equipment measuring total thickness variation is identified, and the servo motor settling, and wafer total thickness variation measurement accuracy are analyzed. As for the experimental variables, vacuum, acceleration time, and speed are changed to find the most efficient value by comparing the stabilization time. The smaller the vacuum and the larger the radius, the longer the settling time. If the radius is small, high-speed rotation performance is good, and if the radius is large, low-speed rotation performance is good. In the future, we plan to conduct an experiment to measure the entire of the wafer.

The electrostatic chuck is a technology that uses electroadhesion to attach objects and is widely used in semiconductor and display processes. This research conducted Maxwell by varying parameters to examine the distribution and variations of chucking force in a bipolar-type ESC. The parameters that were changed include the material properties of the dielectric layer and attachment substrate, applied voltage to the electrode, and the gap and width between the electrodes. The analysis results showed that as the relative permittivity of the dielectric layer and substrate increased, the chucking force also increased, with the relative permittivity of the substrate having a greater impact on the chucking force. And increasing the applied voltage led to an increase in both the chucking force and its rate of change. Lastly, as the gap between the electrodes increased, the chucking force rapidly decreased until a certain distance, after which the decrease became less significant. On the contrary, increasing the electrode width resulted in a rapid increase in the chucking force until a certain width, beyond which the increase became less pronounced, eventually converging to a chucking force of 1700 Pa. This paper is expected to have high potential for the development and research of ESC for OLED deposition.

In this article, a plate-type megasonic cleaning system with cooling pins is proposed for the sliced ingot, which is a raw material of silicon (Si) wafers. The megasonic system is operated with a lead zirconate titanate (PZT) actuator, which has high electric resistance, thus when it is being operated, it dissipates much heat. So this article proposes a megasonic system with cooling pins. In the design process, finite element analysis was performed and the results were used for the design of the waveguide. The frequency with the maximum impedance value was 998 kHz, which agreed well with the measured value of 997 kHz with 0.1 % error. Based on the results, the 1 MHz waveguide was fabricated. Acoustic pressures were measured, and analyzed. Finally, cleaning tests were performed, and 90 % particle removal efficiency (PRE) was achieved over 10 W power. These results imply that the developed 1 MHz megasonic will effectively clean sliced ingot wafer surfaces.

Moiré patterns emerge due to the interference between the non-emission area of the LED screen and the grid line in an image sensor of a video recording device when taking a video in the presence of the LED screen. To reduce the moiré intensity, we have fabricated an anti-moiré filter using hollow glass microspheres (HGMs) by slot-die coating. The LED screen has a large non-emission area because of a large pitch (distance between LED chips), causing more severe moiré phenomenon, compared with a display panel having a very narrow black matrix (BM). It is shown that HGMs diffuse light in such a way that the periodicity of the screen is broken and thus the moiré intensity weakens. To quantitatively analyze its moiré suppression capability, we have calculated the spatial frequencies of the moiré fringes using fast Fourier transform. It is addressed that the moiré phenomenon is suppressed and thus the amplitude of each discrete spatial frequency term is reduced as the HGM concentration is increased. Using the filter with the HGM concentration of 9 wt%, the moiré fringes appeared depending sensitively on the distance between the LED screen and the camera are almost completely removed and the visibility of a nature image is enhanced at a sacrifice of luminance.

Recently, in the problem of multi-order processing in logistics warehouses, multi-pickup systems are changing from the form in which workers walk around the warehouse to the form in which goods come to workers. These changes are shortening the time to process multiple orders and increasing production. This study considered the sequence problem of which warehouse the items to be loaded on each truck come first and which items to be loaded first when loading multiple pallet-unit goods on multiple trucks in an industrial smart logistics automation warehouse. To solve this problem efficiently, we use the mutant algorithm, which combines the GA algorithm and ACO algorithm, and compare with original system.

Currently, the method of loading items in the warehouse, the worker directly decides the loading location, and the most used method is to load the product at the location closest to the entrance. This can be effective when there is no difference in the required amount for goods, but when there is a difference in the required amount for goods, it is inefficient because items with a small required amount are loaded near the entrance and occupy the corresponding space for a long time. Therefore, in order to minimize the release time of goods, it is essential to select an appropriate location when loading goods. In this study, a method for determining the loading location by predicting the required amount of goods was studied to select the optimal loading location. Deep learning based bidirectional long-term memory networks (Bi-LSTM) was used to predict the required amount for goods. This study compares and analyzes the release time of goods in the conventional method of loading close to the entrance and in the loading method using the required amount for goods using the Bi-LSTM model.

We investigated the MoS₂ thin film layer by thermolytic deposition and applied it to the silicon solar cells. MoS₂ thin films were made by two methods of dipping and spin coating of (NH₄)₂MoS₄ precursor solution. We implemented two types of substrates of microtextured and nano-microtextured 6-in. Si pn junction wafers. The fabricated MoS₂ thin film layer was analyzed, and solar cells were fabricated by applying the standard silicon solar cell process. The MoS₂ thin film layer of sulfur-deficient form was deposited on the n-type emitter layer, and electrons, which are minority carriers, were well transported at the interface and exhibited photovoltaic solar cell characteristics. The cell efficiencies were achieved at 5% for microtextured wafers and 2.56% for nano-microtextured wafers.

We designed a novel ultra-miniaturized microcolumn structure having an stigmator to significantly improve throughput per unit time, which is the biggest disadvantage of microcolumns. We adopted the structure of the stigmator in the form of an electrostatic octupole electrode, and used an electrostatic quadrupole deflector with a relatively simple structure considering the increase in wiring due to the introduction of the stigmator. We have dramatically reduced the effect of astigmatism that occurs when the electron beam probe is scanned to the periphery of the target by introducing the stigmator between the control electrode and the deflector. As our numerical analysis simulation results, the field of view obtained as a result of this study is about 46.3% improved compared to our previous study, and the electron beam probe size of less than 10 nm was achieved in the entire field of view.

With the advancement in technology and rapid increase in the demand for microelectromechanical systems (MEMS) based inertial measurement units (IMUs), high-volume production and test system remain a major challenge for the MEMS industry. To compete with the challenging market of Industry 4.0, here we developed an automatic test system to evaluate the performance of the ovenized IMU sensors as well as analyze the data. The automatic test system was developed by interfacing a commercial MEMS IMU (BMI 088) using LabVIEW. The BMI 088 was tested experimentally for long-term bias stability, ON/OFF bias repeatability, and root mean square (rms) noise. Furthermore, the data was analyzed through the developed test system. The results show that the automatic test system has improved the test time and reduced human effort. The developed automatic test system is a significant approach to MEMS research and development (R&D) to increase and improve the mass production of IMUs.

In this paper, a dye-sensitized solar cell (DSSC), one of the representative third-generation solar cells with eco-friendly materials and processes compared to other solar cells, was modeled using MATLAB/Simulink. The simulation was conducted by designating values of series resistance, parallel resistance, light absorption coefficient, and thin film electrode thickness, which are directly related to the efficiency of dye-sensitized solar cells, as arbitrary experimental values. In order to analyze the performance of dye-sensitized solar cells, the optimal value among each parameter experimental value related to efficiency was found using formulas for fill factor (FF) and conversion efficiency.

As the DeFi market continues to expand, fraudulent activities using smart contracts have also increased. HoneyPot and Ponzi schemes are well-known frauds that exploit smart contracts. While several studies have demonstrated the potential to detect smart contracts implementing these scams, there has been a lack of research focusing on simultaneously detecting both types of fraud. This paper addresses this gap by harnessing artificial intelligence to conduct experiments for the detection of both HoneyPot and Ponzi schemes. The study employs the CNN (Convolutional Neural Network) model, commonly used for malware detection. To effectively utilize CNN, the bytecode of smart contracts is transformed into visual representations. The experimental results showcase a recall rate of 0.89 and an F1 score of 0.85, indicating promising detection capabilities.

This paper proposes a lightweight trace-driven Processing-In-Memory (PIM) simulator, TP-Sim. TP-Sim is a General Purpose PIM (GP-PIM) simulator that evaluates various PIM system performance-related metrics. Based on instruction and memory traces extracted from the Intel Pin tool, TP-Sim can replay trace files for multiple models of PIM architectures to compare its performance. To verify the availability of TP-Sim, we estimated three different system configurations on the STREAM benchmark. Compared to the traditional Host CPU-only systems with conventional memory hierarchy, simple GP-PIM architecture achieved better performance; even the Host CPU has the same number of in-order cores. For further study, we also extend TP-Sim as a part of a heterogeneous system simulator that contains CPU, GPGPU, and PIM as its primary and co-processors.

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

We describe an optimal alignment method for improving accuracy of dynamic spectroscopic polarimeter based on monolithic polarizing interferometer. The dynamic spectroscopic polarimeter enables real-time measurements of spectral ellipsometric parameters by using a spectral carrier frequency concept. However, the non-polarizing beam splitter used in the monolithic polarizing interferometer cannot maintain the polarization state perfectly due to phase retardation caused by optical anisotropic characteristics of the non-polarizing beam splitter, resulting in degraded measurement accuracy. The effect of the beam splitter can be minimized through optimal alignment of the polarizers used in the polarizing interferometer and the analyzer. We demonstrate how much the proposed alignment method can enhance the measurement accuracy by comparing with previous alignment approach.

We have investigated the effect of the substrate temperature and oxygen flow rate on the characteristics of IGZO thin films for the TCO (transparent conducting oxide). For this purpose, IGZO thin films were deposited by RF magnetron sputtering at room temperature and 300℃ with various O₂ flow rate. Experiments were carried out while varying the oxygen gas flow rate from 0sccm to 1.0sccm to see how the oxygen gas affects the IGZO thin films. IGZO thin films deposited at room temperature and 300℃ showed amorphous. The lowest resistivity value was 2125x10^-3 Ωcm when the IGZO film was deposited at RT and set up at 0.1sccm. As the oxygen vacancy rate decreased, the resistivity intended to increase. In conclusion, Oxygen vacancy affects the IGZO thin film's electrical characteristic.

Organic photo Diodes (OPDi) give multiple advantages in the growing interest of the flexible optoelectronic devices. Organic semiconductors are freeform as they can deposit on any substrate, so it could be flexible. But the inorganic material photodiodes (PDs) are usually fabricated on silicon wafers which are solid. So, normally PDs are inflexible. By those reasons, we decided to make the vacuum deposited small molecule OPDi. We have investigated the OPDi's J-V characteristic by changing the thickness of p-type layer of OPDi. This device consists of indium-tin-oxide (ITO) / 2,3:6,7-dibenzanthracene (pentacene) / buckminsterfullerene (C60) / aluminum (Al). Its J-V characteristics were measured in the probe station(4156C) that can give dark condition while measuring. And for the luminance characteristics, the photocurrent was measured with the bright halogen lamp and a probe station.

The importance of load-to-use latency has been highlighted as state-of-the-art computing cores adopt deep pipelines and high clock frequencies. The cascaded cache was recently proposed to reduce the access cycle of the L1 cache by utilizing differences in latencies among banks of the cache structure. However, this study assumes the cache is comprised of SRAM, making it unsuitable for direct application to non-volatile memory-based systems. This paper proposes a novel mechanism and structure for lowering dynamic energy consumption. It inserts monitoring logic to keep track of swap operations and write counts. If the ratio of swap operations to total write counts surpasses a set threshold, the cache controller skips the swap of cache blocks, which leads to reducing write operations. To validate this approach, experiments are conducted on the non-volatile memory-based cascaded cache. The results show a reduction in write operations by an average of 16.7% with a negligible increase in latencies.

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

In this study, experimental approach of the measurement of condensation and evaporation heat transfer coefficients is discussed for mixed refrigerants using in the ultra low-temperature cooling system for semiconductor etching process. An experimental apparatus was described performing the condensation and evaporation heat transfer measurements for mixed refrigerants. The mixed refrigerant used in this study was composed of the optimal mixture determined in previous research, with a composition of Ar:R14:R23:R218 = 0.15:0.4:0.15:0.3. The experiments were conducted over a temperature range from -82℃ to 15℃ and at pressures ranging from 18.5 bar to 5 bar. The convection heat transfer coefficients of the mixed refrigerant were measured at flow rates corresponding to actual operating conditions. The condensation heat transfer coefficient ranged from approximately 0.7 to 0.9 kW/m2K, while the evaporation heat transfer coefficient ranged from 1.0 to 1.7 kW/m2K. The detailed discussion of the experimental methods, procedures, and results were described in this paper.

In this paper, we propose a method to design and implement a smart pipeline information management system that can provide visualization information linked to GIS and roadmap based on the construction of precise pipeline buried information. The smart pipeline information management system consists of a positioning device for high-precision pipeline location measurement and surround view image data recording, a database for data storage and management, and a mobile app for remote monitoring and management. It connects surrounding image data and location data with GIS and roadmap. Convenience and accessibility of management can be improved.

Plasma-resistant ceramic (PRC) is a material used to prevent internal damage in plasma processing equipment for semiconductors and displays. The challenge is to suppress particles falling off from damaged surfaces and increase retention time in order to improve productivity and introduce the latest miniaturization process. Here, we confirmed the effect of suppressing plasma deterioration and reducing the etch rate through surface treatment of existing PRC with an initial illumination level of 200 nm. In particular, quartz glass showed a decrease in etch rate of up to 10%. Furthermore, it is believed that micro-scale secondary particles formed on the microstructure of each material grow as crystals during the fluoridation process. This is a factor that can act as a killer defect when dropped, and is an essential consideration when analyzing plasma resistance. The plasma etching suppression effect of the initial illumination is thought to be due to partial over etching at the dihedral angle of the material due to the sputtering of re-emission of Ar+-based cations. This means that plasma damage due to densification can also be interpreted in existing PRC studies. The research results are significant in that they present surface treatment conditions that can be directly applied to existing PRC for mass production and a new perspective to analyze plasma resistance in addition to simple etching rates.

Artificial intelligence is becoming a global hot topic and is being actively applied in various industrial fields. Not only is artificial intelligence being applied to industrial sites in an on-premises method, but cloud-based artificial intelligence platforms are expanding into "as a service" type. The purpose of this study is to develop and verify a measurement tool for an evaluation framework for the adoption of a cloud-based artificial intelligence platform and test the interrelationships of evaluation variables. To achieve this purpose, empirical testing was conducted to verify the hypothesis using an expanded technology acceptance model, and factors affecting the intention to adopt a cloud-based artificial intelligence platform were analyzed. The results of this study are intended to increase user awareness of cloud-based artificial intelligence platforms and help various industries adopt them through the evaluation framework.

In this paper, we identify performance issues in executing compute kernels from PolyBench, which includes compute kernels that are the core computational units of various data-intensive workloads, such as deep learning and data-intensive applications, on Processing-in-Memory (PIM) devices. Therefore, using our in-house simulator, we measured and compared the various performance metrics of workloads based on traditional out-of-order and in-order processors with Processing-in-Memory-based systems. As a result, the PIM-based system improves performance compared to other computing models due to the short-term data reuse characteristic of computational kernels from PolyBench. However, some kernels perform poorly in PIM-based systems without a multi-layer cache hierarchy due to some kernel's long-term data reuse characteristics. Hence, our evaluation and analysis results suggest that further research should consider dynamic and workload pattern adaptive approaches to overcome performance degradation from computational kernels with long-term data reuse characteristics and hidden data locality.

This paper presents a novel approach to multi-focus image fusion using light field cameras. The proposed neural network, LFFCNN (Light Field Focus Convolutional Neural Network), is composed of three main modules: feature extraction, feature fusion, and feature reconstruction. Specifically, the feature extraction module incorporates SPP (Spatial Pyramid Pooling) to effectively handle images of various scales. Experimental results demonstrate that the proposed model not only effectively fuses a single All-in-Focus image from images with multi focus images but also offers more efficient and robust focus fusion compared to existing methods.

The potential performance benefits of Silicon Carbide(SiC) MOSFETs in high power, high frequency power switching applications have been well established over the past 20 years. In the past few years, SiC MOSFET offerings have been announced by suppliers as die, discrete, module and system level products. In high-voltage SiC vertical devices, major design concerns is the edge termination and cell pitch design Field Limiting Rings(FLR) based structures are commonly used in the edge termination approaches. This study presents a comprehensive analysis of the impact of variation of FLR and JFET region on the performance of a 3.3 kV SiC MOSFET during. The improvement in MOSFET reverse bias by optimizing the field ring design and its influence on the nominal operating performance is evaluated. And, manufacturability of the optimization of the JFET region of the SiC MOSFET was also examined by investigating full-map electrical characteristics.

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.