Real-time control of reactive ion etching using neural networks

Stokes, D.; May, G.S.

doi:10.1109/66.892633

Cited by 30 publications

(13 citation statements)

References 24 publications

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“…ANNs have been applied extensively to the area of plasma etch for fault detection [19], modeling [20], and control [21], and have been shown to yield superior estimation accuracy over statistical techniques for some data sets [22].…”

Section: B Artificial Neural Network (Anns)mentioning

confidence: 99%

Global and Local Virtual Metrology Models for a Plasma Etch Process

Lynn

Ringwood

MacGearailt

2012

IEEE Trans. Semicond. Manufact.

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Abstract-Virtual metrology (VM) is the estimation of metrology variables that may be expensive or difficult to measure using readily available process information. This paper investigates the application of global and local VM schemes to a data set recorded from an industrial plasma etch chamber. Windowed VM models are shown to be the most accurate local VM scheme, capable of producing useful estimates of plasma etch rates over multiple chamber maintenance events and many thousands of wafers. Partial least-squares regression, artificial neural networks, and Gaussian process regression are investigated as candidate modeling techniques, with windowed Gaussian process regression models providing the most accurate results for the data set investigated.Index Terms-Gaussian process regression, local modeling, neural network applications, plasma etch, virtual metrology (VM).

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Section: B Artificial Neural Network (Anns)mentioning

confidence: 99%

Global and Local Virtual Metrology Models for a Plasma Etch Process

Lynn

Ringwood

MacGearailt

2012

IEEE Trans. Semicond. Manufact.

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“…Polynomial ANNs were shown by Kim et al to outperform MLPs for etch rate prediction, using chuck gap, RF power, bias, and fraction as network inputs. ANNs have also been used to produce inverse models for etch rate (i.e., etch rate manipulated inputs), which can be used for real-time control [68]. A paper by Su et al [69] looks at a variety of ANN architectures against the accuracy and real-time requirements of R2R process control.…”

Section: ) Statistical Analysismentioning

confidence: 99%

“…The authors report an 83% improvement in etch depth results compared to a purely timed etch. A model-based feedback controller is reported in [68] and [100], controlling etch rate, which is measured using laser inferometry and a profilometer. Manipulated variables are pressure, RF power, and gas flow, and a linear LQG/LTR controller is compared to a nonlinear adaptive controller based on a neural network model.…”

Section: ) Control Of Etch Variablesmentioning

confidence: 99%

Estimation and Control in Semiconductor Etch: Practice and Possibilities

Ringwood

Lynn

Bacelli

et al. 2010

IEEE Trans. Semicond. Manufact.

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Abstract-Semiconductor wafer etching is, to a large extent, an open-loop process with little direct feedback control. Most silicon chip manufacturers rely on the rigorous adherence to a "recipe" for the various etch processes, which have been built up based on considerable historical experience. However, residue buildup and difficulties in achieving consistent preventative maintenance operations lead to drifts and step changes in process characteristics. This paper examines the particular technical difficulties encountered in achieving consistency in the etching of semiconductor wafers and documents the range of estimation and control techniques currently available to address these difficulties. An important feature of such an assessment is the range of measurement options available if closed-loop control is to be achieved.Index Terms-Closed-loop control, plasma etch, run-to-run control, semiconductor wafer, state estimation, virtual metrology.

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“…In addition, it has some limit in the process control and optimization application, especially processes real-time control due to the model size and complexity [4][5][6]. So as Correspondence: hlyou@mail.xidian.edu.cn an alternative to first principle models, empirical models based on statistical design of experiment plays an important role in plasma etching control and optimization [7]. In the research on statistical modeling of semiconductor process, neural network has shown more advantages than response surface methodology (RSM) for plasma etching in accuracy and robustness.…”

Section: Introductionmentioning

confidence: 99%

Combined D-optimal design and generalized regression neural network for modeling of plasma etching rate

You

Chen

Liu

et al. 2014

Int. J. Metrol. Qual. Eng.

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Abstract. Plasma etching process plays a critical role in semiconductor manufacturing. Because physical and chemical mechanisms involved in plasma etching are extremely complicated, models supporting process control are difficult to construct. This paper uses a 35-run D-optimal design to efficiently collect data under well planned conditions for important controllable variables such as power, pressure, electrode gap and gas flows of Cl2 and He and the response, etching rate, for building an empirical underlying model. Since the relationship between the control and response variables could be highly nonlinear, a generalized regression neural network is used to select important model variables and their combination effects and to fit the model. Compared with the response surface methodology, the proposed method has better prediction performance in training and testing samples. A success application of the model to control the plasma etching process demonstrates the effectiveness of the methods.

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Real-time control of reactive ion etching using neural networks

Cited by 30 publications

References 24 publications

Global and Local Virtual Metrology Models for a Plasma Etch Process

Global and Local Virtual Metrology Models for a Plasma Etch Process

Estimation and Control in Semiconductor Etch: Practice and Possibilities

Combined D-optimal design and generalized regression neural network for modeling of plasma etching rate

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