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Conference on Advanced Etch Technology for Nanopatterning IX

作者： Kearney, Kara Chopra, Sonali Zhu, Xilan Ban, Yang Bonnecaze, Roger T. Chopra, Meghali C. SandBox Semicond Austin TX 78704 USA

ISBN: (纸本)9781510634268

As the critical dimensions (CDs) of etch profiles continue to decrease, precise control of plasma etch processing becomes increasingly important. Achieving this control requires optimizing etch recipes, which is time consuming and expensive as an extensive amount of experiments must be performed. Here we present a method for the prediction of process windows to achieve target CDs for high aspect ratio trenches using model-based experimental design. A reduced-order model of the physics and chemistry of the etch is used to identify the best experiments to perform to calibrate the model. The model is then used to efficiently explore the process parameter space to identify the largest ranges of process parameters that achieve desired ranges of CDs. The methodology is practically demonstrated on a three-step trench etch through three layers of material consisting of spin-on-glass, spin-on-carbon and silicon. It is found that this physics-model based method requires less than half as many experiments to identify the optimal etch recipe than full-factorial design of experiments.

关键词： Multi-layer etch etch recipe model-based experimental design

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Development and implementation of systematic model-development strategy using model-based experimental design

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CHEMICAL ENGINEERING RESEARCH & design 2019年 146卷 290-310页

作者： Jiang, Zhengkun Portha, Jean-Francois Commenge, Jean-Marc Falk, Laurent Univ Lorraine Lab React & Genie Proc CNRS 1 Rue GrandvilleBP 20451 F-54001 Nancy France

In this paper, a systematic model-development strategy using model-based experimental design, consisting of initial data acquisition, model development, model identification, model validation and model refining, is developed. model development is based on the reaction network proposed within the reaction supernetwork containing all feasible chemical reactions and mass transfers. For model identification, validation and refining, model-based experimental design is performed by taking into consideration several reactors, which enlarges the explored experimental windows. based on this strategy, a computer-aided software, facilitating the user's work, is developed for model development of liquid-phase reaction systems. The application of the strategy is demonstrated with an experimental case study involving NaOH-catalyzed ethanolysis of sunflower oil. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： model development Reaction supernetwork model-based experimental design Multi-equipment strategy Ethanolysis

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Towards a model-based experimental design of the Maturation Process of Biohybrid Heart Valves 8th

Towards a Model-Based Experimental Design of the Maturation ...

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8th Conference on Foundations of Systems Biology in Engineering (FOBSE)

作者： Voss, Kirsten Pyta, Lorenz Gesenhues, Jonas Mela, Petra Schmitz-Rode, Thomas Abel, Dirk Rhein Westfal TH Aachen Inst Automat Control Aachen Germany Rhein Westfal TH Aachen Inst Appl Med Engn Aachen Germany

model-based experimental designs minimize the experimental effort while maximizing the amount of analyzable experimental data. In this paper, an initial mathematical model of the spatially distributed maturation process of tissue engineered heart valves is developed for a model-based experimental design. The model contains the state variables cell amount, collagen concentration and elastin concentration. based on the developed model, a variance-based sensitivity analysis using Sobol's method is performed. The results indicate that all model parameters influence the model solution, while the variance of the parameters related to cell diffusion comprises an exceeding influence. Consequently, corresponding experiments should especially focus on those parameters. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Distributed parameter systems Tissue engineering Numerical analysis Mathematical models model-based experimental design Sensitivity analysis

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Towards a model-based experimental design of the Maturation Process of Biohybrid Heart Valves ⁎

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IFAC-PapersOnLine 2019年第26期52卷 113-120页

作者： Kirsten Voß Lorenz Pyta Jonas Gesenhues Petra Mela Thomas Schmitz-Rode Dirk Abel Institute of Automatic Control RWTH Aachen University Germany Institute of Applied Medical Engineering RWTH Aachen University Germany

model-based experimental designs minimize the experimental effort while maximizing the amount of analyzable experimental data. In this paper, an initial mathematical model of the spatially distributed maturation process of tissue engineered heart valves is developed for a model-based experimental design. The model contains the state variables cell amount, collagen concentration and elastin concentration. based on the developed model, a variance-based sensitivity analysis using Sobol’s method is performed. The results indicate that all model parameters influence the model solution, while the variance of the parameters related to cell diffusion comprises an exceeding influence. Consequently, corresponding experiments should especially focus on those parameters.

关键词： Distributed parameter systems Tissue engineering Numerical analysis Mathematical models model-based experimental design Sensitivity analysis

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Graph-based Bayesian Optimization for Large-Scale Objective-based experimental design

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第10期33卷 5913-5925页

作者： Imani, Mahdi Ghoreishi, Seyede Fatemeh George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Univ Maryland Inst Syst Res ISR College Pk MD 20742 USA

design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.

关键词： Uncertainty Genomics Bioinformatics Bayes methods Task analysis Computational modeling Tuning Bayesian optimization gene regulatory networks (GRNs) graphical models mean objective cost of uncertainty (MOCU) model-based experimental design

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Optimal experimental design of physical property measurements for optimal chemical process simulations

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FLUID PHASE EQUILIBRIA 2022年 557卷 113420-113420页

作者： Fleitmann, Lorenz Pyschik, Jan Wolff, Ludger Schilling, Johannes Bardow, Andre Swiss Fed Inst Technol Dept Mech & Proc Engn Energy & Proc Syst Engn Tannenstr 3 CH-8092 Zurich Switzerland Rhein Westfal TH Aachen Inst Tech Thermodynam Schinkelstr 8 D-52062 Aachen Germany Forschungszentrum Julich Inst Energy & Climate Res Energy Syst Engn IEK 10 Wilhelm Johnen Str D-52425 Julich Germany

Chemical process simulations depend on physical properties, which are usually available through property models with parameters estimated from experiments. The required experimental effort can be reduced using the method of Optimal experimental design (OED). OED reduces the number of experiments by minimising the expected uncertainty of the estimated parameters. In chemical engineering, however, the main purpose of an experiment is usually not to determine property parameters with minimum uncertainty but to simulate processes accurately. Therefore, this paper presents the OED of physical property measurements resulting in the most accurate process simulations: c-optimal experimental design (c-OED). c-OED aims to minimise the uncertainty of the process simulation results, which is estimated by linear uncertainty propagation from uncertain property parameters through the process model. We use c-OED to design liquid-liquid equilibrium and diffusion experiments minimising thermodynamic and economic performance metrics of three solvent-based processes. In all three case studies, the c-optimal design substantially reduces the experimental effort for the same simulation accuracy compared to state-of-the-art OED that neglects the process. Our findings are confirmed by a Monte-Carlo simulation of the designed experiments. Furthermore, we assess the limits of c-OED for highly nonlinear process models. Thus, the work shows how c-OED can successfully reduce experimental effort required for accurate process simulations by tailoring experimental designs to the process model. (C) 2022 The Author(s). Published by Elsevier B.V.

关键词： model-based experimental design Simulation uncertainty Parameter precision Liquid-liquid-equilibrium Extraction-distillation

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Sequential model-based design of experiments for development of mathematical models for thin film deposition using chemical vapor deposition process

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CHEMICAL ENGINEERING RESEARCH & design 2020年 160卷 74-88页

作者： Shahmohammadi, Ali Bonnecaze, Roger T. Univ Texas Austin McKetta Dept Chem Engn 200 E Dean Keeton StStop C0400 Austin TX 78712 USA

experimental data is required to estimate unknown parameters for a mathematical model describing thin film growth and to optimize the chemical vapor deposition (CVD) process. The most informative experimental data can be obtained by sequential model-based design of experiments (MBDoE), but this requires significant effort to implement compared to factorial design of experiments (DoE). For CVD processes it is not clear which approach is better or more efficient. Here, we compare the effectiveness of sequential A-optimal (MBDoE) with a factorial (DoE) using a case study involving a mechanistic model of thin-film deposition. The sequential A-optimal MBDoE and factorial DoE are compared based on their effectiveness in obtaining accurate parameter estimates and model predictions. Synthetic experimental data is generated from simulations with the true parameters plus noise. The results suggest that the A-optimal MBDoE is slightly better than factorial DoE for estimating model parameters. The parameter estimates from each experimental design approach are used to solve a model-based optimization problem. The results indicate that model-based optimization from the model with parameter estimates obtained by the A-optimal design gives a significantly better estimate of the optimal process conditions than factorial DoE. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Chemical vapor deposition Thin film Mathematical models model-based experimental design Factorial experimental design

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Parameter estimation and optimization of a loosely bound aggregating pharmaceutical crystallization using in situ infrared and laser backscattering measurements

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2004年第19期43卷 6168-6181页

作者： Togkalidou, T Tung, HH Sun, Y Andrews, AT Braatz, RD Univ Illinois Urbana IL 61801 USA Merck & Co Inc Rahway NJ 07065 USA

model-based experimental design and parameter estimation, coupled with in situ infrared and laser backscattering instrumentation, was applied to the cooling crystallization of a drug compound to construct a model for use in the design of an optimal operating procedure. Because the model was constructed completely from in situ sensor data, with no sampling procedures, the amount of pharmaceutical compound required for batch process development was significantly reduced. modeling issues involving the selection of the expressions for the supersaturation and nucleation rate, as well as the electronics mode in the laser backscattering instrument, were explored. All but one of the kinetic parameters from the gray-box model constructed using laser backscattering data were close to the kinetic parameters for a first-principles model constructed from data from off-line particle size measurements. The model was used to design batch operating procedures to minimize nucleation and maximize the sharpness of the crystal size distribution. This appears to be the first time that the procedure of model-based experimental design, parameter estimation, model selection, and optimization has been applied to the crystallization of a pharmaceutical, as well as the first time that this procedure has been applied to a crystallization process that forms loosely bound aggregates.

关键词： .loosely Off-line nucleation rate supersaturation Parameter Estimation crystal size First-Principles gray-box situ infrared model-based experimental design Pharmaceutical Crystallization operating procedure electronics mode

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