• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2535-2542
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• 2020

To understand the fundamental parameters of Alvand tokamak, A Rogowski coil with an active integrator was designed and constructed. Considering the characteristics of the Alvand tokamak, the structural and electrical parameters affecting the sensor function, were designed. Calibration was performed directly in the presence of plasma. The sensor has a high resistance against interference of external magnetic fields. Plasma current was measured in various experiments. Based on the plasma current profile and loop voltage signal, the time evolution of plasma discharge was investigated and plasma behavior was analyzed. Alvand tokamak discharge was divided into several regions that represents different physical phenomena in the plasma. During the plasma discharge time, plasma had significant changes and its characteristic was not uniform. To understand the plasma behavior in each of the phases, the Rogowski sensor should have sufficient time resolution. The Rogowski sensor with a frequency up to 15 kHz was appropriate for this purpose.

• Journal of Industrial Technology
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• v.33 no.A
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• pp.67-72
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• 2013

CNT(carbon nanotube) humidity sensors with plasma treated electrodes exhibit a much faster response time and a higher sensitivity to humidity, compared to untreated CNT and porous Cr electrodes. These results may be partially due to their percolated pore structure being more accessible for water molecules and for expending the diffusion of moisture to the polyimide sensing film, and partially due to the oxygenated surface of CNT films. This paper shows a plasma process effect and selectivity characteristics of CNT film humidity sensor.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.98-105
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• 2008

In this paper, the system for monitoring process of aluminum laser welding was developed using the light signal emitted from the plasma which comes from interaction between material and laser. Photodiode for monitoring system was selected based on the spectrum analysis of light from plasma and keyhole. Behavior of plasma and keyhole was analyzed through the sensor signals. Value of sensor signal represented the light intensity and fluctuation of signal indicated the stability of plasma and keyhole. For the relation between welding condition and sensor signals, the input power and weld geometry greatly effected on the average of each sensor signals. Using the feature values of signals, estimation model for tensile strength of weld was formulated with neural network algorithm. Performance of this model was verified through coefficient of determination and average error rate.

• Journal of information and communication convergence engineering
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• v.3 no.4
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• pp.187-190
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• 2005

High aspect ratio via-hole etching process has emerged as one of the most crucial means to increase component density for ULSI devices. Because of charge accumulation in via-hole, this sophisticated and important process still hold several problems, such as etching stop and loading effects during fabrication of integrated circuits. Indeed, the concern actually depends on accumulated charge. For monitoring accumulated charge during plasma etching process, charge-up monitoring sensor was fabricated and tested under some plasma conditions. This paper presents a neural network-based technique for analyzing and modeling several electrical performance of plasma charge-up monitoring sensor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.303-306
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• 2005

High aspect ration via-hole etching process has emerged as one of the most crucial means to increase component density for ULSI devices. Because of charge accumulation in via hole, this sophisticated and important process still hold several problems, such as etching stop, loading effects during fabrication of integrated circuits. Indeed, the concern actually depends on accumulated charge. For monitoring accumulated charge during plasma etching process, charge-up monitoring sensor was fabricated and tested under some plasma conditions. This paper presents a neural network-based technique for analyzing and modeling several electrical performance of plasma charge-up monitoring sensor.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.132-140
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• 2000

Plasma arc cutting is one of the most widely used processes in metal cutting fields and is a process that produces parted metal plates by cutting them with an arc plasma established between the electrode tip and the plate(workpiece). When the tip-to-workpiece distance varies during cutting, the cut quality, for example the kerf width, is deteriorated by the change of plasma arc. The variations of tip-to-workpiece distance are due to the different factors such as inaccuracies in setting the torch or workpiece, thermal distortions during cutting, and uneven surface of workpiece. The control to keep the tip-to-workpiece distance constant is thus indispensable to improve the flexibility of automatic plasma arc cutting system applications. In this study, an arc sensor which utilizes the electrical signal obtained from the plasma arc itself was developed. The arc sensor has an advantage that no particular sensing device is necessary and real-time sensing of the tip-to-workpiece distance is possible directly under the plasma arc. The relationship between plasma arc voltage and tip-to-workpiece distance was determined through the repeated experimental results. The model was used for developing an automatic tip-to-workpiece distance control system of plasma arc cutting. It could be shown that the proposed system has a successful capability of tip-to-workpiece distance control.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.43-43
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• 2010

We have characterized the parametric and functional properties of live cell and cancer cell according to plasma treatment conditions using Atmospheric Pressure (AP) Plasma with uniquely designed low temperature arc-free unit. AP plasma system showed very highly efficient capabilities of reacting and interfacing directly with live and cancer cells. The parametric results with the types of gases, applied power, applied gap, and process times on cells will be presented in accordance with functional studies of the works. The growth of cancer cells is directly influenced by AP plasma exposure with evaluating plasma conditions in several human cancer cells and understanding how plasma exposure alters molecular signaling pathways. The cells exhibit a slower or faster growth rates compared with untreated cells, depending on the cell types. These results strongly support the conclusion that alterations in one or more of each gene are responsible, at least in part, for plasma-induced apoptosis in cancer cells. In addition, it also will be presented that AP plasma has an important role for the improvement of sensor performance due to excellent interface property between enzyme and metal electrode for bio sensor manufacturing process.

• 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.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.99-101
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• 2006

Auto-Cross Correlated time series (ACTS) model was constructed by using the backpropagation neural network. The performance of ACTS model was evaluated with sensor information collected from a large volume, industrial plasma-enhanced chemical vapor deposition system. A total of 18 sensor information were collected. The effect of inclusion of past and future information were examined. For all but three sensor information with a large data variance demonstrated a prediction error less than 3%. By integrating ACTS model into equipment software, process quality can be more stringently monitored while improving device throughput.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.8
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• pp.1266-1272
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• 2003

This paper presents a new class of magnetic microsensors based on the Hall effect in AC microplasma. In the theoretical study, we develop a simple model of the plasma Hall sensor and express the plasma Hall voltage as a function of magnetic field, plasma discharge field, pressure, and electrode geometry. On this basis, we have designed and fabricated magnetic microsensors using AC neon plasma. In the experiment, we have measured the Hall voltage output of the plasma microsensors for varying five different conditions, including the frequency and the magnitude of magnetic field, the frequency and the magnitude of plasma discharge voltage, and the neon pressure. The fabricated magnetic microsensors show a magnetic field sensitivity of 8.87${\pm}$0.18㎷/G with 4.48% nonlinearity.