• Korean Journal of Materials Research
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• v.34 no.6
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• pp.303-308
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• 2024

The semiconductor and display industries require the development of plasma resistant materials for use in high density plasma etching process equipment. Yttria (Y₂O₃) is a ceramic material mainly used to ensure good plasma resistance properties, which requires a dense microstructure. In commercial production, a sintering process is applied to reduce the sintering temperature of Y₂O₃. In this study, the effect of the addition of glass frit to the sintered specimen was examined when manufacturing yttria sintered specimens for semiconductor process equipment parts. The Y₂O₃ specimen was shaped into a Ø50 mm size and then sintered at 1,600 ℃ for 1~8 h. The characteristics, X-ray diffraction pattern, densities, contraction rate of the specimen, and swelling of the surface of the Y₂O₃ specimens were investigated as a function of the sintering time and glass frit addition. The Y₂O₃ specimen exhibited a density of over 4.9 g/cm³ as the sintering time increased, and the swelling phenomenon characteristics were improved by glass frit, by controlling particle size.

• Journal of the Semiconductor & Display Technology
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• v.23 no.3
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• pp.140-147
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• 2024

This paper explores the recent advancements in speech recognition technology, focusing on the integration of artificial intelligence to improve recognition accuracy in challenging environments, such as noisy or low-quality audio conditions. Traditional speech recognition methods often suffer from performance degradation in noisy settings. However, the application of deep neural networks (DNN) has led to significant improvements, enabling more robust and reliable recognition in various industries, including banking, automotive, healthcare, and manufacturing. A key area of advancement is the use of Silent Speech Interfaces (SSI), which allow communication through non-speech signals, such as visual cues or other auxiliary signals like ultrasound and electromyography, making them particularly useful for individuals with speech impairments. The paper further discusses the development of multi-modal speech recognition, combining both audio and visual inputs, which enhances recognition accuracy in noisy environments. Recent research into lip-reading technology and the use of deep learning architectures, such as CNN and RNN, has significantly improved speech recognition by extracting meaningful features from video signals, even in difficult lighting conditions. Additionally, the paper covers the use of self-supervised learning techniques, like AV-HuBERT, which leverage large-scale, unlabeled audiovisual datasets to improve performance. The future of speech recognition technology is likely to see further integration of AI-driven methods, making it more applicable across diverse industries and for individuals with communication challenges. The conclusion emphasizes the need for further research, especially in languages with complex morphological structures, such as Korean

• Journal of the Microelectronics and Packaging Society
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• v.23 no.2
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• pp.79-84
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• 2016

A flexible electronic device deformed by external force causes the failure of a semiconductor die. Even without failure, the repeated elastic deformation changes carrier mobility in the channel and increases resistivity in the interconnection, which causes malfunction of the integrated circuits. Therefore it is desirable that a semiconductor die be placed on a neutral line where the mechanical stress is zero. In the present study, we investigated the effects of design factors on the position of neutral line by finite element analysis (FEA), and expected the possible failure behavior in a flexible face-down packaging system assuming flip-chip bonding of a silicon die. The thickness and material of the flexible substrate and the thickness of a silicon die were considered as design factors. The thickness of a flexible substrate was the most important factor for controlling the position of the neutral line. A three-dimensional FEA result showed that the von Mises stress higher than yield stress would be applied to copper bumps between a silicon die and a flexible substrate. Finally, we suggested a designing strategy for reducing the stress of a silicon die and copper bumps of a flexible face-down packaging system.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.12
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• pp.1249-1255
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• 2011

In modern large-scale semiconductor manufacturing clean rooms, the energy consumed by the outdoor air-conditioning system during heating, humidification, cooling, and dehumidification of the incoming outdoor air represents about 45% of the total air-conditioning load required to maintain a clean-room environment. In particular, the energy required for humidification of the outdoor air in winter is very high. Therefore, evaluation and comparison of the energy consumption in key humidification systems, viz., steam-humidification and water-spray-humidification systems, used in outdoor air-conditioning systems would be useful to reduce the outdoor air-conditioning load in clean rooms. In the present study, an experiment with an outdoor air flow of 1000 $m^3$/h was conducted to compare the air-conditioning process and energy consumption in outdoor air-conditioning systems with electrodeboiler steam humidifiers and air-washer water spray humidification systems. The experimental results showed that the water-spray-humidification-type outdoor air-conditioning system consumed less electrical power than did the steam-humidification-type system and was more energy efficient during winter.

• Resources Recycling
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• v.15 no.5 s.73
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• pp.26-32
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• 2006

The waste solution discharged from the LCD manufacturing process contains acids like nitric, acetic and phosphoric acid and metal ions such as Al, Mo and other impurities. It is important to remove impurities less than 1 ppm in phosphoric acid to reuse as an etchant because the residual impurities even in sub-ppm concentration in semiconductor materials play a major role on the electronic properties. In this study, a mixed system of solvent extraction, diffusion dialysis and ion-exchange was developed to commercialize in an efficient system fur recovering the high-purity phosphoric acid. By vacuum evaporation, almost 99% of nitric and acetic acid was removed. And by solvent extraction method with tri-octyl phosphate (TOP) as an extractant, the removal of acetic and nitric acid from the acid mixture was achieved effectively at the ratio A/O=1/3 with 4th stage of extraction stage. About 97.5% of Al and 36.7% of Mo were removed by diffusion dialysis. Essentially almost complete removal of metal ions and purification of high-purity phosphoric acid could be obtained by using ion exchange.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.42-46
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• 2016

In LCD Display or semiconductor manufacturing processes, the anti-static technology of glass substrates and wafers becomes one of the most difficult issues which influence the yield of the semiconductor manufacturing. In order to overcome the problems of wafer surface contamination various issues such as ionization in decompressed vacuum and inactive gas(i.e. $N_2$ gas, Ar gas, etc.) environment should be considered. Soft X ray radiation is adequate in air and $O_2$ gas at atmospheric pressure while UV radiation is effective in $N_2$ gas Ar gas and at reduced pressure. At this point of view, the "vacuum ultraviolet ray ionization" is one of the most suitable methods for static elimination. The vacuum ultraviolet can be categorized according to a short wavelength whose value is from 100nm to 200nm. this is also called as an Extreme Ultraviolet. Most of these vacuum ultraviolet is absorbed in various substances including the air in the atmosphere. It is absorbed substances become to transit or expose the electrons, then the ionization is initially activated. In this study, static eliminator based on the vacuum ultraviolet ray under the above mentioned environment was tested and the results show how the ionization performance based on vacuum ultraviolet ray can be optimized. These vacuum ultraviolet ray performs better in extreme atmosphere than an ordinary atmospheric environment. Neutralization capability, therefore, shows its maximum value at $10^{-1}{\sim}10^{-3}$ Torr pressure level, and than starts degrading as pressure is gradually reduced. Neutralization capability at this peak point is higher than that at reduced pressure about $10^4$ times on the atmospheric pressure and by about $10^3$ times on the inactive gas. The introductions of these technology make it possible to perfectly overcome problems caused by static electricity and to manufacture ULSI devices and LCD with high reliability.

• Proceedings of the Korean Institute of Resources Recycling Conference
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• 2006.05a
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• pp.90-94
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• 2006

The waste solution discharged from the LCD manufacturing process contains acids like nitric, acetic and phosphoric acid and metal ions such as Al, Mo and other impurities. It is important to removal of impurities to tess than 1ppm in phosphoric acid to reuse as an etchant because the residual impurities even in sub-ppm concentration in semiconductor materials play a major role on the electronic properties. In this study, we have been clearly established that a mixed system of solvent extraction, diffusion dialysis and ion-exchange technique, which made individually the most of characteristics is developed to commercialize in an efficient system for recovering the high-purity phosphoric acid. By applying vacuum evaporation, the yield of the process are almost 99% removal of nitric acid and acetic acid was achieved. And by applying the solvent extraction method with tri-octyl phosphate(TOP) as an extractant, the removal of acetic and nitric acid from the acid mixture was achieved effectively at the ratio O/A=1/3 with four stages and the stripping of nitric acid from organic phase is attained at a ration of O/A=1 with six stages by distilled water. About 97% and 76% removal of Al and Mo were achieved by diffusion dialysis. Essentially complete less than 1ppm removal of Al, Mo by using ion exchange ion resin and purification of the phosphoric acid was obtain.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.3
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• pp.9-15
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• 2021

Miniaturization of semiconductor devices has recently faced a physical limitation. To overcome this, 3D packaging in which semiconductor devices are vertically stacked has been actively developed. 3D packaging requires three unit processes of TSV, wafer grinding, and bonding, and among these, copper bonding is becoming very important for high performance and fine-pitch in 3D packaging. In this study, the effects of Ti nanolayer on the antioxidation of copper surface and low-temperature Cu bonding was investigated. The diffusion rate of Ti into Cu is faster than Cu into Ti in the temperature ranging from room temperature to 200℃, which shows that the titanium nanolayer can be effective for low-temperature copper bonding. The 12nm-thick titanium layer was uniformly deposited on the copper surface, and the surface roughness (R_q) was lowered from 4.1 nm to 3.2 nm. Cu bonding using Ti nanolayer was carried out at 200℃ for 1 hour, and then annealing at the same temperature and time. The average shear strength measured after bonding was 13.2 MPa.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.27-30
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• 2022

The plasma process, which has many advantages in terms of efficiency and environment compared to conventional process methods, is widely used in semiconductor manufacturing. Plasma Sheath is a dark region observed between the plasma bulk and the chamber wall surrounding it or the electrode. The Plasma Sheath Monitoring Sensor (PSMS) measures the difference in voltage between the plasma and the electrode and the RF power applied to the electrode in real time. The PSMS data, therefore, are expected to have a high correlation with the state of plasma in the plasma chamber. In this study, a model for predicting the state of nitrogen ions in the plasma chamber is training by a deep learning machine learning techniques using PSMS data. For the data used in the study, PSMS data measured in an experiment with different power and pressure settings were used as training data, and the ratio, flux, and density of nitrogen ions measured in plasma bulk and Si substrate were used as labels. The results of this study are expected to be the basis of artificial intelligence technology for the optimization of plasma processes and real-time precise control in the future.