A Calculation Method of Deposition Profiles in Chemical Vapor Deposition Reactors Using Genetic Algorithms for The Autom
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A Calculation Method of Deposition Profiles in Chemical Vapor Deposition Reactors Using Genetic Algorithms for The Automatic Modeling System of Reaction Mechanisms Takahiro Takahashi and Yoshinori Ema Department of Electrical and Electronic Engineering, Faculty of Engineering, Shizuoka University, 3-5-1 Johoku, Hamamatsu 432-8561 JAPAN ABSTRACT Fast and accurate calculation of the predicted results of Chemical Vapor Depositions (CVD) is very helpful to the high-throughput optimization of the CVD processes. In addition, robustness of the calculation process is important for automation of the optimization process. Therefore, we have developed a novel calculation method, by which robust and accurate calculations along with reduced computing cost were achieved, to reproduce deposition profiles in a macroscopic cavity (macrocavity). Boundary value problems for estimating diffusion-reaction equations by iterations of numerical integrations were changed into problems of finding the linear combinations consisted of a few functions. The coefficients of the linear combinations were optimized by Genetic Algorithms (GA). We could demonstrate the validity of the proposed method using various examples of the reaction mechanisms and conditions.
INTRODUCTION Chemical Vapor Deposition (CVD) is one of the most important manufacturing processes used in the semiconductor industry. One important goal of CVD research and development efforts is to identify the reaction model (reaction mechanism) that reveals the reaction path from source gases to films, both quantitatively and qualitatively. [1] Because automation of identifying the reaction model is very helpful to the high-throughput optimization of the process parameters of CVD, we developed an automatic modeling system of the reaction mechanisms, which we call Evolutionary Analysis for Reaction Systems (EARS). [2-5] Although we successfully demonstrated the validity of EARS, running EARS requires a huge calculation cost for the repetitions needed in estimating deposition profiles by solving difficult (stiff) diffusion-reaction equations. The huge calculation cost inhibits the applications of the system to various CVD processes. Therefore, we propose a novel calculation method using Genetic Algorithms (GA) to estimate the deposition profiles in a macroscopic cavity (macrocavity). [1,6-7] In addition, we have applied the method to calculating conditions for some reaction
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models and have successfully demonstrated that the method reproduces the deposition profiles with both smaller calculation costs and greater accuracy than the conventional method.
THEORETICAL DETAILS In this study, the reaction model consists of deposition species, films, gas-phase reactions with values of the rate constants and surface reactions with values of the rate constants. The reactions of the model are the first-order reactions of the species. We assigned the number to the deposition species and the reactions. The number 1 indicates the source gas and all other numbers indica
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