Correction to "Real-Time Carbon Content Control for PECVD ZrO$_2$Thin-Film Growth"

Ni, Dong; Lou, Yifei; Christofides, P.D.; Sha, Lin; Lao, S.; Chang, Jane P.

doi:10.1109/tsm.2004.834218

Cited by 6 publications

(6 citation statements)

References 1 publication

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“…In practice, real-time estimates of film porosity can be obtained from a combination of in situ gas phase measurements and off-line thin-film porosity measurements. While it is difficult to obtain real-time direct measurements of film porosity, it is possible to use open-loop experimental data at different deposition conditions (including off-line porosity measurements) to construct a model that estimates film porosity from in-situ gas-phase measurements and combine this model with the model predictive control system proposed in this work; the reader may refer to ref for an implementation of this approach in the context of thin-film carbon content measurement and control.…”

Section: Model Predictive Controller Designmentioning

confidence: 99%

Simulation and Control of Porosity in a Three-Dimensional Thin-Film Solar Cell

Huang

Orkoulas

Christofides

2013

Ind. Eng. Chem. Res.

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This work focuses on the simulation and control of a three-dimensional (3D) porous silicon thin-film deposition process that is used in the manufacture of thin-film solar cell systems. Initially, a solid-by-solid 3D kinetic Monte Carlo (kMC) model is introduced to simulate the porous silicon thin-film deposition process, and the simulation parameters are tuned to generate porous silicon films with porosity values that match available experimental data. A closed-form differential equation model then is introduced to predict the dynamics of the film porosity computed by the kMC model, and the parameters in this model are identified by appropriate fitting to open-loop kMC simulation results. Subsequently, a model predictive controller (MPC) is designed and implemented on the kMC model. Closed-loop simulation results demonstrate that the thin-film porosity can be regulated to desired values.

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Section: Model Predictive Controller Designmentioning

confidence: 99%

Simulation and Control of Porosity in a Three-Dimensional Thin-Film Solar Cell

Huang

Orkoulas

Christofides

2013

Ind. Eng. Chem. Res.

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“…Furthermore, the development of modern surface roughness measurement techniques provides the opportunity to obtain surface roughness measurements in real-time using spectroscopic ellipsometry techniques (Zapien et al, 2001), grazing-incidence small-angle X-ray scattering (GISAXS) (Renaud et al, 2003) or by combination of on-line measurement techniques for measuring gas phase compositions with off-line measurement techniques for measuring surface roughness. An implementation of the latter approach can be found in Ni et al (2004), where it was used to measure carbon composition of thin films in plasma-enhanced chemical vapor deposition (PECVD) using combination of optical emission spectroscopy (OES) and X-ray photoelectron spectroscopy (XPS). Also, experimental methods have been developed to perform scanning tunneling microscopy (STM) measurements of the surface during epitaxial growth of semiconductor layers (Voigtländer, 2001).…”

Section: Introductionmentioning

confidence: 99%

Model parameter estimation and feedback control of surface roughness in a sputtering process

Lou

Christofides

2008

Chemical Engineering Science

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“…[5][6][7][8] The development of modern roughness measurement techniques provides the opportunity to obtain roughness measurements in real-time using spectroscopic ellipsometry techniques, 9 by grazing-incidence small-angle X-ray scattering (GISAXS), 10 or by combination of on-line measurement techniques for measuring gas-phase compositions with off-line measurement techniques for measuring surface roughness. An implementation of the latter approach was recently reported in ref 11 where it was used to measure carbon composition of thin films in plasma-enhanced chemical vapor deposition using combination of optical emission spectroscopy (OES) and X-ray photoelectron spectroscopy (XPS). Also, experimental methods have been developed to perform scanning tunneling microscopy (STM) measurement on the surface during epitaxial growth of semiconductor layers.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Feedback Control of Surface Roughness Using a Stochastic PDE: Design and Application to a Sputtering Process

Lou

Christofides

2006

Ind. Eng. Chem. Res.

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In this work, we develop a method for nonlinear feedback control of the roughness of a one-dimensional surface whose evolution is described by the stochastic Kuramoto−Sivashinsky equation (KSE), a fourth-order nonlinear stochastic partial differential equation. We initially formulate the stochastic KSE into a system of infinite nonlinear stochastic ordinary differential equations by using Galerkin's method. A finite-dimensional approximation of the stochastic KSE is then derived that captures the dominant mode contribution to the surface roughness. A nonlinear feedback controller is then designed based on the finite-dimensional approximation to control the surface roughness. An analysis of the closed-loop nonlinear infinite-dimensional system is performed to characterize the closed-loop performance enforced by the nonlinear feedback controller in the closed-loop infinite-dimensional system. The effectiveness of the proposed nonlinear controller and the advantages of the nonlinear controller over a linear controller resulting from the linearization of the nonlinear controller around the zero solution are demonstrated through numerical simulations. Finally, a successful application of a stochastic KSE-based nonlinear feedback controller to the kinetic Monte Carlo model of a sputtering process is also demonstrated.

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Correction to "Real-Time Carbon Content Control for PECVD ZrO$_2$Thin-Film Growth"

Cited by 6 publications

References 1 publication

Simulation and Control of Porosity in a Three-Dimensional Thin-Film Solar Cell

Simulation and Control of Porosity in a Three-Dimensional Thin-Film Solar Cell

Model parameter estimation and feedback control of surface roughness in a sputtering process

Nonlinear Feedback Control of Surface Roughness Using a Stochastic PDE: Design and Application to a Sputtering Process

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