Optimization of Chemical Vapor Deposition Process

George, Pradeep; Gea, Hae Chang; Jaluria, Yogesh

doi:10.1115/detc2006-99748

Cited by 6 publications

(13 citation statements)

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“…A detailed comparison between experimental and numerical results has been made and fairly good agreements has been found (Chiu, 1999). The numerical results using the settings described in the previous discussion is almost identical to George's simulation results (George, 2007) with the mass diffusivity in the FLUENT database, which is given by a polynomial equation of the temperature as follows: …”

Section: Validationsupporting

confidence: 66%

“…To validate the correctness of the numerical simulations with the developed settings in FLUENT, the deposition rate of the silicon in a horizontal CVD reactor is compared with experimental and numerical results from other researchers (Eversteyn et al, 1970;Mahajan and Wei, 1991;Yoo and Jaluria, 2002;George, 2007). The Fig.…”

Section: Validationmentioning

confidence: 99%

“…The partial pressure of the silane is 124.1 Pa under the atmospheric pressure, providing the information of the mass fraction of the silane. The operating temperature is 300 K and the susceptor is isothermally heated at 1323 K. The material properties and the kinetics of the chemical reactions are given from (George, 2007) otherwise from the FLUENT database. The rest of the settings and the utilized solvers remain the same as the previous discussion.…”

Section: Validationmentioning

confidence: 99%

“…Simulation and optimization of CVD systems have been studied by many researchers (Mouche et al, 1995;Southwell et al, 1996;Chiu, 1999;Raja et al, 2000;Chiu et al, 2002;Ly and Tran, 2002;George, 2007;). However, design uncertainties can found everywhere in the CVD process.…”

Section: Optimization Of the Cvd Processes And Existence Of The Desigmentioning

confidence: 99%

“…For example, the deposition uniformity itself is a subjective scale of the quality of the CVD production. George (George, 2007) utilized a weighted sum of the local slopes of the deposition to quantify its uniformity. Lin et al ) used some standard statistical measures, including the root mean square and the kurtosis, as the responses of the uniformity factors.…”

Section: Design Variables and Responses Of The Cvd Processesmentioning

confidence: 99%

See 4 more Smart Citations

Systematic Strategy for Modeling and Optimization of Thermal Systems With Design Uncertainties

Jaluria¹,

Gea²,

Lin³

2010

Frontiers in Heat and Mass Transfer

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Thermal systems play significant roles in the engineering practice and our lives. To improve those thermal systems, it is necessary to model and optimize the design and the operating conditions. More importantly, the design uncertainties should be considered because the failures of the thermal systems may be very dangerous and produce large loss. This review paper focuses on a systematic strategy of modeling and optimizing of the thermal systems with the considerations of the design uncertainties. To demonstrate the proposed strategy, one of the complicated thermal systems, Chemical Vapor Deposition (CVD), is simulated, parametrically modeled, and optimized. The operating conditions, inlet velocities and susceptor temperatures, are the most significant factors in the CVD and are chosen as the design variables. Several responses -including the percentage of the working area, the mean of the deposition rate, the root mean square of the deposition, and the surface kurtosis -are chosen based on the physical needs and statistical foundations, and are utilized to represent the productivity and the quality of the thin-film deposition. One of the Response Surface Method (RSM), the Radial Basis Function (RBF), is employed to formulate the objective and constraint functions for the optimization. However, it is not until the design uncertainties are considered that the thermal system designs have high risk of the violations of the performance constraints. The Reliability-Based Design Optimization (RBDO) algorithms are used to solve the optimization problems with the design uncertainties. The most famous RBDO methods are the Reliability Index Approach (RIA) and the Performance Measure Approach (PMA). In RBDO, probabilistic constraints are established with respect to either normally or non-normally distributed random variables. The optimal solutions are found subjected to the allowable level of the failure probabilities. The Monte Carlo Simulation (MCS) results can be used to evaluate the failure probabilities. As a result, the proposed systematic strategy of parametrically modeling and optimizing with design uncertainties can be applied to either experiments or simulations of other thermal systems to quantitatively represent the performances, improve their productivity, maintain the quality control, and reduce the probability of the system failure.

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Section: Validationsupporting

confidence: 66%

Section: Validationmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Section: Optimization Of the Cvd Processes And Existence Of The Desigmentioning

confidence: 99%

Section: Design Variables and Responses Of The Cvd Processesmentioning

confidence: 99%

See 3 more Smart Citations

Systematic Strategy for Modeling and Optimization of Thermal Systems With Design Uncertainties

Jaluria¹,

Gea²,

Lin³

2010

Frontiers in Heat and Mass Transfer

Self Cite

View full text Add to dashboard Cite

show abstract

Design of Thermal Systems

Jaluria¹

2017

Handbook of Thermal Science and Engineering

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A Filter-Based Sample Average SQP for Optimization Problems With Highly Nonlinear Probabilistic Constraints

Hsu

Chan

2010

Journal of Mechanical Design

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In this work, we develop a filter-based sequential quadratic programming (SQP) algorithm for solving reliability-based design optimization (RBDO) problems with highly nonlinear constraints. The proposed filter-based SQP uses the approach of average importance sampling (AAIS) in calculating the values and gradients of probabilistic constraints. AAIS allocates samples at the limit state boundaries such that relatively few samples are required in calculating constraint probability values to achieve high accuracy and low variance. The accuracy of probabilistic constraint gradients using AAIS is improved by a sample filter that eliminates sample outliers that have low probability of occurrence and high gradient values. To ensure convergence, the algorithm uses an iteration filter in place of the penalty function to avoid the ill-conditioning problems of the penalty parameters in the acceptance of a design update. A sample reuse mechanism that improves the efficiency of the algorithm by avoiding redundant samples is introduced. The “unsampled” region, the region not covered by previous samples, is identified using iteration step lengths, the trust region, and constraint reliability levels. As a result, the filter-based sampling SQP efficiently handles highly nonlinear probabilistic constraints with multiple most probable points or functions without analytical forms. Several examples are demonstrated, and the results are compared with those from first order reliability method/second order reliability method and Monte Carlo simulations. Results show that by integrating the modified AAIS with the filter-based SQP, the overall computation cost of solving RBDO problems can be significantly reduced.

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Optimization of Chemical Vapor Deposition Process

Cited by 6 publications

References 0 publications

Systematic Strategy for Modeling and Optimization of Thermal Systems With Design Uncertainties

Systematic Strategy for Modeling and Optimization of Thermal Systems With Design Uncertainties

Design of Thermal Systems

A Filter-Based Sample Average SQP for Optimization Problems With Highly Nonlinear Probabilistic Constraints

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