• Industrial Engineering and Management Systems
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• 제14권4호
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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.

• 한국전기전자재료학회논문지
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• 제25권3호
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• pp.176-181
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• 2012

Due to the high etch rate and low fabrication cost, the wet etching of silicon using KOH etchant is widely used in MEMS fabrication area. However, anisotropic etch characteristic obstruct intuitional mask design and compensation structures are required for mask design level. Therefore, the accurate modeling for various types of silicon surface is essential for fabrication of three-dimensional MEMS structure. In this paper, we modeled KOH etch profile for MEMS based energy harvester using fuzzy logic. Modeling results are compared with experimental results and it is applied to design of compensation structure for MEMS based energy harvester. Through Fuzzy inference approaches, developed model showed good agreement with the experimental results with limited etch rate information.

• Applied Science and Convergence Technology
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• 제23권4호
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• pp.169-178
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• 2014

Fluid model based numerical analysis is done to simulate a low damage etch back system for 20 nm scale semiconductor fabrication. Etch back should be done conformally with very high material selectivity. One possible mechanism is three steps: reactive radical generation, adsorption and thermal desorption. In this study, plasma generation and transport steps are analyzed by a commercial plasma modeling software package, CFD-ACE+. Ar + $CF_4$ ICP was used as a model and the effect of reactive gas inlet position was investigated in 2D and 3D. At 200~300 mTorr of gas pressure, separated gas inlet scheme is analyzed to work well and generated higher density of F and $F_2$ radicals in the lower chamber region while suppressing ions reach to the wafer by a double layer conducting barrier.

• 한국전기전자재료학회논문지
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• 제18권1호
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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.

• 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.

• Journal of Information Processing Systems
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• 제10권3호
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• pp.429-442
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• 2014

In this paper, we investigated the use of seasonal autoregressive integrated moving average (SARIMA) time series models for fault detection in semiconductor etch equipment data. The derivative dynamic time warping algorithm was employed for the synchronization of data. The models were generated using a set of data from healthy runs, and the established models were compared with the experimental runs to find the faulty runs. It has been shown that the SARIMA modeling for this data can detect faults in the etch tool data from the semiconductor industry with an accuracy of 80% and 90% using the parameter-wise error computation and the step-wise error computation, respectively. We found that SARIMA is useful to detect incipient faults in semiconductor fabrication.

• ETRI Journal
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• 제30권3호
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• pp.383-393
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• 2008

The etching mechanism of $ZrO_2$ thin films and etch selectivity over some materials in both $BCl_3$/Ar and $BCl_3/CHF_3$/Ar plasmas are investigated using a combination of experimental and modeling methods. To obtain the data on plasma composition and fluxes of active species, global (0-dimensional) plasma models are developed with Langmuir probe diagnostics data. In $BCl_3$/Ar plasma, changes in gas mixing ratio result in non-linear changes of both densities and fluxes for Cl, $BCl_2$, and ${BCl_2}^+$. In this work, it is shown that the non-monotonic behavior of the $ZrO_2$ etch rate as a function of the $BCl_3$/Ar mixing ratio could be related to the ion-assisted etch mechanism and the ion-flux-limited etch regime. The addition of up to 33% $CHF_3$ to the $BCl_3$-rich $BCl_3$Ar plasma does not influence the $ZrO_2$ etch rate, but it non-monotonically changes the etch rates of both Si and $SiO_2$. The last effect can probably be associated with the corresponding behavior of the F atom density.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 하계학술대회 논문집 Vol.4 No.1
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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.

• 한국전기전자재료학회논문지
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• 제15권1호
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• pp.7-14
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• 2002

In this paper, neutral networks are used to build models of oxide film etched In CHF$_3$/CF$_4$ with a magnetically enhanced reactive ion etcher(MERIE). A statistical 2$\^$4-1/ experimental design plus one center point was used to characterize relationships between process factors and etch responses. The factors that were varied include radio frequence(rf) power, pressure, CHF$_3$ and CF$_4$ flow rates. Resultant 9 experiments were used to train neural networks and trained networks were subsequently tested on its appropriateness using additionally conducted 8 experiments. A total of 17 experiments were thus conducted for this modeling. The etch responses modeled are dc bias voltage, etch rate and etch uniformity A qualitative, good agreement was obtained between predicted and observed behaviors.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1485-1488
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• 1997

The capacity of memory chip has increased vert quickly and 64MDRAM becomes main product in semiconductor manufacturing lines consists of many sequential processes, including etching process. although it needs direct sensing of wafer state for the accurae detching, it depends on indirect esnsing and sample test because of the complexity of the plasma etching. This equipment receives the inner light of etch chamber through the viewport and convets it to the voltage inetnsity. In this paper, EDP voltage signal has a new role to detect etching failure. First, we gathered data(EPD sigal, etching time and etchrate) and then analyzed the relationships between the signal variatin and the etch rate using two neural network modeling. These methods enable to predict whether ething state is good or not per wafer. For experiments, it is used High Density Inductive coupled Plasma(HDICP) ethcing equipment. Experiments and results proved to be abled to determine the etching state of wafer on-line and analyze the causes by modeling and EPD signal data.