• Journal of the Korean Physical Society
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• 제73권10호
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• pp.1519-1528
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• 2018

The objective of the present study is the development of a comprehensive air chemical kinetic model that includes 11 species and 54 chemical reactions for the numerical investigation of air nonequilibrium inductively coupled plasmas. The two-dimensional, compressible Navier-Stokes equations coupled with the electromagnetic-field equations were employed to describe the fundamental characteristics of an inductive plasma. Dunn-Kangs 32 chemical-reaction model of air was reconstructed and used as a comparative model. The effects of the different chemical kinetic models on the flow field were analyzed and discussed at identical/different working pressures. The results theoretically indicate that no matter the working pressure is low or high, the use of the 54 chemical kinetic model presented in this study is a better choice for the numerical simulation of a nonequilibrium air ICP.

• Journal of international Conference on Electrical Machines and Systems
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• 제3권1호
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• pp.78-82
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• 2014

Power Transformers with more than one secondary winding are not uncommon in industrial applications. But new classes of applications where very large number of independent secondaries are used are becoming popular in controlled converters for medium and high voltage applications. Cascade H-bridge medium voltage drives and Pulse Step Modulation (PSM) based high voltage power supplies are such applications. Regulated high voltage power supplies (Fig. 1) with 35-100 kV, 5-10 MW output range with very fast dynamics (${\mu}S$ order) uses such transformers. Such power supplies are widely used in fusion research. Here series connection of isolated voltage sources with conventional switching semiconductor devices is achieved by large number of separate transformers or by single unit of multi-secondary transformer. Naturally, a transformer having numbers of secondary windings (~40) on single core is the preferred solution due to space and cost considerations. For design and simulation analysis of such a power supply, the model of a multi-secondary transformer poses special problem to any circuit analysis software as many simulation softwares provide transformer models with limited number (3-6) of secondary windings. Multi-Secondary transformer models with 3 different schemes are available. A comparison of test results from a practical Multi-secondary transformer with a simulation model using magnetic component is found to describe the behavior closer to observed test results. Earlier models assumed magnetising inductance in a linear loss less core model although in actual it is saturable core made-up of CRGO steel laminations. This article discusses a more detailed representation of flux coupled magnetic model with saturable core properties to simulate actual transformers very close to its observed parameters in test and actual usage.

• 한국표면공학회지
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• 제46권3호
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• pp.133-138
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• 2013

We have developed a 2D axi-symmetric numerical model for an inductively coupled plasma system in order to analyze gas mixing effect through a narrow gap shower head. For frictional flow, holes of 0.5 mm diameter and 2 mm length are approximately modeled in 2D. Gas velocity distribution 10 mm below the shower head showed 2 times difference between the center and the edge at 10 mTorr. At 10 mm above the wafer, it was increased to 6 times difference due to the pumping duct effect. The model with a 5 mm height buffer region of a shower head showed reasonable behavior of Ar discharge. The density of Ar metastable showed additional peak inside the buffer region around the edge holes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 A
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• pp.163-165
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• 1996

In the PIC simulation of plasma, the fields are commonly calculated on uniform spatial grids using FDM. But, FDM has a difficulty in modeling a complex shaped model. FEM has a good flexibiblity in treating a complex shape, so that we calculated the field by using FEM not FDM. In this paper, the plasma between plane-to-plane electrodes was simulated using FEM and FDM. Comparing the results of those two methods told us that FEM is also valid as a calculating method in PIC plasma simulation. In order to verify the use of FEM, the discharge of rod-to-plane was simulated. There was not a little distortion of the electric field between the electrodes due to the distribution of space charges.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.

• Applied Science and Convergence Technology
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• 제23권5호
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• pp.265-272
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• 2014

The ion energy distributions (IEDs) arriving at a substrate strongly affect the etching rates in plasma etching processes. In order to determine the IEDs accurately, it is important to obtain the characteristics of radio frequency (rf) sheath at pulsed rf substrates. However, very few studies have been conducted to investigate pulsing effect on IEDs at multiple rf driven electrodes. Therefore, in this work, we extended previous one-dimensional dynamics model for pulsed-bias electrodes. We obtained the IEDs using the developed rf sheath model and observed that numerically solved IEDs are in a good agreement with the experimental results.

• 한국전기전자재료학회논문지
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• 제23권11호
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• pp.831-836
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• 2010

In this paper, Plasma Enhanced Chemical Vapor Deposition (PECVD) $SiO_2$ film properties are modeled using statistical analysis and neural networks. For systemic analysis, Box-Behnken's 3 factor design of experiments (DOE) with response surface method are used. For characterization, deposited film thickness and film stress are considered as film properties and three process input factors including plasma RF power, flow rate of $N_2O$ gas, and flow rate of 5% $SiH_4$ gas contained at $N_2$ gas are considered for modeling. For film thickness characterization, regression based model showed only 0.71% of root mean squared (RMS) error. Also, for film stress model case, both regression model and neural prediction model showed acceptable RMS error. For sensitivity analysis, compare to conventional fixed mid point based analysis, proposed sensitivity analysis for entire range of interest support more process information to optimize process recipes to satisfy specific film characteristic requirements.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• 반도체디스플레이기술학회지
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• 제18권2호
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• pp.48-52
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• 2019

Virtual metrology, which is one of APC techniques, is a method to predict characteristics of manufactured films using machine learning with saving time and resources. As the photoresist is no longer a mask material for use in high aspect ratios as the CD is reduced, hard mask is introduced to solve such problems. Among many types of hard mask materials, amorphous carbon layer(ACL) is widely investigated due to its advantages of high etch selectivity than conventional photoresist, high optical transmittance, easy deposition process, and removability by oxygen plasma. In this study, VM using different machine learning algorithms is applied to predict the thickness of ACL and trained models are evaluated which model shows best prediction performance. ACL specimens are deposited by plasma enhanced chemical vapor deposition(PECVD) with four different process parameters(Pressure, RF power, $C_3H_6$ gas flow, $N_2$ gas flow). Gradient boosting regression(GBR) algorithm, random forest regression(RFR) algorithm, and neural network(NN) are selected for modeling. The model using gradient boosting algorithm shows most proper performance with higher R-squared value. A model for predicting the thickness of the ACL film within the abovementioned conditions has been successfully constructed.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2010년 춘계학술대회논문집
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• pp.444-445
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• 2010

Predictive modeling schemes have been developed to characterize the heat Transfer and thermo-mechanical behavior for the plasma display panel (PDP) in operation. The inverse approach was adopted to predict the accurate temperature distribution and deformation in PDP. The predictive models were validated with the measurements from real panel. The developed models could be utilized to predict and/or improve the product quality of PDP.