• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.1
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• pp.1-5
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• 2005

A new model of plasma etch process was constructed by using a radial basis function network (RBFN). This technique was applied to an etching of silicon carbide films in a NF$_3$ inductively coupled plasma. Experimental data to train RBFN were systematically collected by means of a 2$^4$ full factorial experiment. Appropriateness of prediction models was tested with test data consisted of 16 experiments not pertaining to the training data. Prediction performance was optimized with variations in three training factors, the number of pattern units, width of radial basis function, and initial weight distribution between the pattern and output layers. The etch responses to model were an etch rate and a surface roughness measured by atomic force microscopy. Optimized models had the root mean-squared errors of 26.1 nm/min and 0.103 nm for the etch rate and surface roughness, respectively. Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20 % and 50 % for the etch rate and surface roughness, respectively. It is therefore expected that RBFN can be effectively used to construct prediction models of plasma processes.

• Journal of the Korean institute of surface engineering
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• v.42 no.3
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• pp.133-138
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• 2009

Effects of bipolar pulse driving frequency between 50 kHz and 250 kHz on the discharge shapes were analyzed by measuring plasma characteristics by OES (Optical Emission Spectroscopy) and Langmuir probe. Plasma characteristics were modeled by a simple electric field analysis and fluid plasma modeling. Discharge shapes by a continuous dc and bipolar pulsed dc were different as a dome-type and a vertical column-type at the cathode. From OES, the intensity of 811.5 nm wavelength, the one of the main peaks of Ar, decreased to about 43% from a continuous dc to 100 kHz. For increasing from 100 kHz to 250 kHz, the intensity of 811.5 nm wavelength also decreased by 46%. The electron density decreased by 74% and the electron temperature increased by 36% at the specific position due to the smaller and denser discharge shape for increasing pulse frequency. Through the numerical analysis, the negative glow shape of a continuous dc were similar to the electric potential distribution by FEM (Finite Element Method). For the bipolar pulsed dc, we found that the electron temperature increased to maximum 10 eV due to the voltage spikes by the fast electron acceleration generated in pre-sheath. This may induce the electrons and ions from plasma to increase the energetic substrate bombardment for the dense thin film growth.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05b
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• pp.1-6
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• 2002

Plasma processing of semiconductor materials plays a dominant role in microelectronic technology. During last century, plasma have gone a way from laboratory phenomena to industrial applications due to intensive progress in both scientific and industrial trends. Improvement and development of new experience together with development of plasma theory and plasma diagnostics methods. A most parameters (pressure, flow rate, power density) and various levels of plasma system (energy distribution, volume gas chemistry, transport, heterogeneous effects) to understand the whole process mechanism. It will allow us to choose a correct ways for processes optimization.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.211-213
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• 2006

In this study, plasma etching process was modeled by using support vector machine (SVM). The data used in modeling were collected from the etching of silica thin films in inductively coupled plasma. For training and testing neural network, 9 and 6 experiments were used respectively. The performance of SVM was evaluated as a function of kernel type and function type. For the kernel type, Epsilon-SVR and Nu-SVR were included. For the function type, linear, polynomial, and radial basis function (RBF) were included. The performance of SVM was optimized first in terms of kernel type, then as a function of function type. Five film characteristics were modeled by using SVM and the optimized models were compared to statistical regression models. The comparison revealed that statistical regression models yielded better predictions than SVM.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.108-110
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• 2006

A prediction model of charge concentration of silicon nitride (SiN) thin films was constructed by using neural network and genetic algorithm. SIN films were deposited by plasma enhanced chemical vapor deposition and the deposition process was characterized by means of $2^{6-1}$ fractional factorial experiment. Effect of five training factors on the model prediction performance was optimized by using genetic algorithm. This was examined as a function of the learring rate. The root mean squared error of optimized model was 0.975, which is much smaller than statistical regression model by about 45%. The constructed model can facilitate a Qualitative analysis of parameter effects on the charge concentration.

• International journal of advanced smart convergence
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• v.6 no.4
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• pp.88-95
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• 2017

The conventional scan methods are based on a laser scanner and a depth camera, which requires high cost and complicated post-processing. Whereas in photometric scanning method, the 3D modeling data is acquired through multi-view images. This is advantageous compared to the other methods. The quality of a photometric 3D model depends on the environmental conditions or the object characteristics, but the quality is lower as compared to other methods. Therefore, various methods for improving the quality of photometric scanning are being studied. In this paper, we aim to investigate the effect of illumination conditions on the quality of photometric scanning data. To do this, 'Moai' statue is 3D printed with a size of $600(H){\times}1,000(V){\times}600(D)$. The printed object is photographed under the hard light and soft light environments. We obtained the modeling data by photometric scanning method and compared it with the ground truth of 'Moai'. The 'Point-to-Point' method used to analyseanalyze the modeling data using open source tool 'CloudCompare'. As a result of comparison, it is confirmed that the standard deviation value of the 3D model generated under the soft light is 0.090686 and the standard deviation value of the 3D model generated under the hard light is 0.039954. This proves that the higher quality 3D modeling data can be obtained in a hard light environment. The results of this paper are expected to be applied for the acquisition of high-quality data.

• Journal of Astronomy and Space Sciences
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• v.33 no.1
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• pp.13-19
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• 2016

In recent years, there has been renewed activity in the study of local plasma density enhancements in the low latitude F region ionosphere (low latitude plasma blobs). Satellite, all-sky airglow imager, and radar measurements have identified the characteristics of these blobs, and their coupling to Equatorial Plasma Bubbles (EPBs). New information related to blobs has also been obtained from the Communication/Navigation Outage Forecasting System (C/NOFS) satellite. In this paper, we briefly review experimental, theoretical and modeling studies related to low latitude plasma blobs.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.470-474
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• 2006

This dissertation discuses about a ferrite core plasma source using low operating frequency without sputtering problem by the stored electric field. Compared with the conventional RF power system with 13.56MHz switching frequency, the proposed plasma power system is only separated at 400kHz, so that it makes possible to use of low cost switching elements, PWM control and soft switching. Moreover, it could improve the coupling efficiency for plasma and antenna by using the ferrite core in order to transfer the energy of the load This dissertation tried to analyze new plasma generation method for the plasma generation system by modeling the plasma load and grafting the concept of impedance matching in order to interpret it with the formula This dissertation verified the ferrite core inductive coupling plasma source authorized for 400kHz of low frequency power by applying to the semi-conductor ash process thru the measurement of ash capacity and uniformed plasma distribution on the actual wafer.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.8
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• pp.697-703
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• 2017

A teflon ionization modeling has been conducted to predict the performance of a PPT(Pulsed Plasma Thruster). One dimensional unsteady circuit model and Teflon ablation model were implemented. The Saha equation was adapted to predict the ionization of Carbon and Fluorine gas. The lumped circuit model including a resistance and a inductance model of a plasma was adapted to predict the magnitude of a discharge current. Numerical simulation results had good agreements with pervious research. The degree of current change according to PPT operating voltage was examined.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.58-62
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• 2003

Silicon carbide (SiC) was etched in a $NF_3/CH_4$ inductively coupled plasma. The etch process was modeled by using a neural network called generalized regression neural network (GRNN). For modeling, the process was characterized by a $2^4$ full factorial experiment with one center point. To test model appropriateness, additional test data of 16 experiments were conducted. Particularly, the GRNN predictive capability was drastically improved by a genetic algorithm (GA). This was demonstrated by an improvement of more than 80% compared to a conventionally obtained model. Predicted model behaviors were highly consistent with actual measurements. From the optimized model, several plots were generated to examine etch rate variation under various plasma conditions. Unlike the typical behavior, the etch rate variation was quite different depending on the bias power Under lower bias powers, the source power effect was strongly dependent on induced dc bias. The etch rate was strongly correated to the do bias induced by the gas ratio. Particularly, the etch rate variation with the bias power at different gas ratio seemed to be limited by the etchant supply.