Prediction of profile surface roughness in CHF3/CF4 plasma using neural network

Kim, Byungwhan; Kim, Kunho

doi:10.1016/s0169-4332(03)00963-2

Cited by 22 publications

(4 citation statements)

References 8 publications

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“…Chamber-related process data such as temperature, pressure, gas flows, and power are typically collected from etch chambers using in-built sensors. Such data has been used by several authors to create empirical input-output models relating chamber inputs to etch rates, etch bias, and uniformity measures [7]. Additional data can be collected by installing more sensors on the etch chamber.…”

Section: Index Terms-gaussian Process Regression Local Modeling Neumentioning

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: Index Terms-gaussian Process Regression Local Modeling Neumentioning

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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“…Kim et al [74] successfully produced an ANN model that predicted the discrepancy in sidewall bottom etch rate compared to center etch rate, using genetic algorithms to optimize the spread values. Surface roughness was modelled, using ANNs, in [75] and [67] using generalized regression and radial basis function networks, respectively. Reference [75] also employed GAs for ANN optimization, while statistical models, for the same application, were found to be significantly inferior in [67].…”

Section: ) Statistical Analysismentioning

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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“…Applications range from control [25], prediction [26][27][28], modelling [29][30][31], fault diagnosis [32], feature extraction [33,34] to engine management [35] and data analysis [36].…”

Section: Introductionmentioning

confidence: 99%