• Archives of Pharmacal Research
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• v.13 no.3
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• pp.221-226
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• 1990

A physiological pharmacokinetic hybrid model was developed in order to predict the disposition kinetics of diphenylhydantoin (DPH) in the brain from the plasma conentration data of DPH. The model was constructed under the assumptions of well-stirred, plasma flow-limited and lienar tissue diposition kinetics of DPH. DPH was administered intravenously to the rats at a dose of 10 mg/kg together with/without sodium salicylate (SA;10 mg/kg) and the DPH concentrations in the plasma and brain were determined. Plasma protein binding of DPH concentrations in the plasma and brain were determined. Plasma protein binding of DPH was also determined using equilibrium dialysis technique. Then the model was tested for its predictability of DPH concentrations in the brian from the plasma data of DPH. It was found that the predicted values of DPH concentrations in the brian were in fair agreement with the experimental values in the rats of both treatments. The 2-fold increase in the brain concentration of DPH by SA-coadinistration was predicted well from the plasma concentration and plasma free fraction ($f_p$) data of DPH using the model. Therefore, the hybrid model was concluded to be very useful for the prediction of the concentrations of DPH in the brain from the plasma concentration data. Finally, DPH concentrations in the human brian was calculated using this model from plasma DPH data in the literature, yet the scale-up of this model to the human is not convinced.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.132-132
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• 2015

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification (FDC) or advanced process control (APC) models on to the real mass production lines efficiently, high performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this study, as an effective method to include the 'good information' representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b-, q-factors and surface passivation parameters as PCs into the PCR based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient data set provided cases. For mass production data of 350 wafers, developed PI based VM (PI-VM) model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.6
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• pp.1-9
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• 2022

This study introduces the calculation model of ionization composition and electrical conductivity for metallic plasma for practical application to modeling and simulation of modern electrical detonators. The present model includes the correction for non-ideality of dense plasma conditions which are expected in electrical explosion of bridge in detonators. The computational results for copper plasma show favorable agreement with experimental data for a wide range of plasma temperature and high density conditions and the model is proper for detonator modeling with good prediction accuracy.

• Journal of Welding and Joining
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• v.18 no.3
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• pp.122-130
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• 2000

Due to the inherent dimensional uncertainty, the tolerances accumulate in the assembly of plasma cutting torch. Tolerance accumulation has serious effect on the performance of the plasma torch. This study proposes a statistical tolerance propagation model, which is based on matrix transform. This model can predict the final tolerance distributions of the completed plasma torch assembly with the prescribed statistical tolerance distribution of each part to be assembled. Verification of the proposed model was performed by making use of Monte Carlo simulation. Monte Carlo simulation generates a large number of discrete plasma torch assembly instances and randomly selects a point within the tolerance region with the prescribed statistical distribution. Monte Carlo simulation results show good agreement with that of the proposed model. This results are promising in that we can predict the final tolerance distributions in advance before assembly process of plasma torch thus provide great benefit at the assembly design stage of plasma torch.

• Applied Science and Convergence Technology
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• v.23 no.4
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• pp.179-186
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• 2014

Fluid model based numerical simulation was carried out for an inductively coupled plasma assisted sputter deposition system. Power absorption, electron temperature and density distribution was modeled with drift diffusion approximation. Effect of an electrically conducting substrate was analyzed and showed confined plasma below the substrate. Part of the plasma was leaked around the substrate edge. Comparison between the quasi-neutrality based compact model and Poisson equation resolved model showed more broadened profile in inductively coupled plasma power absorption than quasi-neutrality case, but very similar Ar ion number density profile. Electric potential was calculated to be in the range of 50 V between a Cr rod source and a conductive substrate. A new model including Cr sputtering by Ar+was developed and used in simulating Cr deposition process. Cr was modeled to be ionized by direct electron impact and showed narrower distribution than Ar ions.

• Particle and aerosol research
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• v.5 no.3
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• pp.123-131
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• 2009

This paper presents simulation results of particle transport in low-pressure, low-temperature plasma environment. The size dependent transport of particles in the plasma is investigated with a two-dimensional simulation tool developed in-house for plasma chamber analysis and design. The plasma model consists of the first two and three moments of the Boltzmann equation for ion and electron fluids respectively, coupled to Poisson's equation for the self-consistent electric field. The particle transport model takes into account all important factors, such as gravitational, electrostatic, ion drag, neutral drag and Brownian forces, affecting the motion of particles in the plasma environment. The particle transport model coupled with both neutral fluid and plasma models is simulated through a Lagrangian approach tracking the individual trajectory of each particle by taking a force balance on the particle. The size dependant trap locations of particles ranging from a few nm to a few ${\mu}m$ are identified in both electropositive and electronegative plasmas. The simulation results show that particles are trapped at locations where the forces acting on them balance. While fine particles tend to be trapped in the bulk, large particles accumulate near bottom sheath boundaries and around material interfaces, such as wafer and electrode edges where a sudden change in electric field occurs. Overall, small particles form a "dome" shape around the center of the plasma reactor and are also trapped in a "ring" near the radial sheath boundaries, while larger particles accumulate only in the "ring". These simulation results are qualitatively in good agreement with experimental observation.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.185.1-185.1
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• 2016

Cutoff probe, diagnostics instrument for plasma density, have been received an extensive attention due to simple, robust and lowest assumption. Although the cutoff probe has a long history, physical model is limited in low density plasma. For that reason, we propose a novel transmission line model of cutoff probe for precise measurement of high density plasma. In addition simplified circuit model can be obtained from transmission line model. It can explain simply physics of cutoff probe in high density plasma.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.33-36
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• 2007

We performed a discharge analysis on ac plasma display panel through the numerical simulation of the EF (Energy Fluid) model using the electron's energy equation. When it is compared to the results of commonly used LFA (Local Field Approximation) model, there is a clear difference in the spatiotemporal distribution of Xe excited species. In particular, the experimentally observed striation phenomena in the anode region could be observed in EF model and the occurrence of the striation was attributed to the ionization and excitation instability due to the streaming electrons in the anode region plasma.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.604-607
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• 2005

In the electro-discharge machining the machining performance is closely related to the characteristics of discharge which can be identified from electrical behavior in gap between workpiece and electrode. Therefore, the accurate prediction of electrical behavior in electro-discharge machining (EDM) is useful to process control and optimization. However, any simulation model fur prediction of electrical behavior in EDM process has never been reported until now. In this study, a simulation model is developed to analyze the electrical behavior of electro-discharge plasma which significantly influences electrical behavior in EDM process. For the purpose of this the fundamentals of electro-discharge mechanism such as inception, propagation, formation of plasma channel and termination are investigated to accurately predict the cycle of discharge plasma in EDM. As a result, a mathematical model of electro-discharge plasma is constructed with considering the fundamentals of electro-discharge plasma. Consequently, it is demonstrated that the developed model can predict the electrical behavior of plasma such as electron density in various conditions.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.268-273
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• 2000

Plasma models are crucial to equipment design and process optimization. A radial basis function network(RBFN) in con-junction with statistical experimental design has been used to model a process plasma. A 2$^4$ full factorial experiment was employed to characterized a hemispherical inductively coupled plasma(HICP) in characterizing HICP, the factors that were varied in the design include source power, pressure, position of shuck holder, and Cl$_2$ flow rate. Using a Langmuir probe, plasma attributes were collected, which include typical electron density, electron temperature. and plasma potential as well as their spatial uniformity. Root mean-squared prediction errors of RBEN are 0.409(10(sup)12/㎤), 0.277(eV), and 0.699(V), for electron density, electron temperature, and Plasma potential, respectively. For spatial uniformity data, they are 2.623(10(sup)12/㎤), 5.704(eV) and 3.481(V), for electron density, electron temperature, and plasma potential, respectively. Comparisons with generalized regression neural network(GRNN) revealed an improved prediction accuracy of RBFN as well as a comparable performance between GRNN and statistical response surface model. Both RBEN and GRNN, however, experienced difficulties in generalizing training data with smaller standard deviation.