• Electrical & Electronic Materials
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• v.15 no.9
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• pp.10-14
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• 2002

Plasma processing plays a significant role in semiconductor devices technology. Development of new plasma systems, such as high-density plasma reactors, required development of plasma theory to understand a whole process mechanism and to be able to explain and to predict processing results. A most important task in this way is to establish interconnections between input process parameters (working gas, pressure flow rate input power density) and a various plasma subsystems (electron gas, volume and heterogeneous gas chemistry, transport), which are closely connected one with other. It will allow select optimal ways for processes optimizations.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.433-440
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• 1998

The purpose of this study is to provide an integrated process control system for plasma etching. The control system is designed to employ neural network for the modeling of plasma etching process and to utilize genetic algorithm to search for the appropriate selection of control input variables, and to provide a control chart to maintain the process output within a desired range in the real plasma etching process. The target equipment is the one operating in DRAM production lines. The result shows that the integrated system developed is practical value in the improved performance of plasma etching process.

• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07a
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• pp.3-6
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• 2002

Plasma processing plays a significant role in semiconductor devices technology. Development of new plasma systems, such as high-density plasma reactors, required development of plasma theory to understand a whole process mechanism and to be able to explain and to predict processing results. A most important task in this way is to establish interconnections between input process parameters (working gas, pressure, flow rate, input power density) and various plasma subsystems (electron gas, volume and heterogeneous gas chemistry, transport), which are closely connected one with other. It will allow select optimal ways for processes optimization.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.392-400
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• 2015

Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.

• Transactions on Electrical and Electronic Materials
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• v.9 no.4
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• pp.156-162
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• 2008

We report results on a study of inductively coupled plasma (ICP) etching of Parylene-C (poly-monochloro-para-xylylene) films using an $O_2$ gas. Effects of process parameters on etch rates were investigated and are discussed in this article from the standpoint of plasma parameter measurements, performed using a Langmuir probe and modeling calculation. Process parameters of interest include ICP source power and pressure. It was shown that major etching agent of polymer films was oxygen atoms O($^3P$). At the same time it was proposed that positive ions were not effective etchant, but ions played an important role as effective channel of energy transfer from plasma towards the polymer.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• 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.

• Proceedings of the Korean Vacuum Society Conference
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• 2008.02a
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• pp.181-181
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• 2008

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.