Global Parameterization of Multiple Point-Defect Dynamics Models in Silicon
The task of determining globally robust estimates for the thermophysical properties of intrinsic point defects in crystalline silicon remains challenging. Previous attempts at point-defect model regression have focused on the use of a single type of experimental data but as of yet no single paramete...
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Published in | Journal of the Electrochemical Society Vol. 150; no. 11; pp. G673 - G682 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
01.11.2003
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Online Access | Get full text |
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Summary: | The task of determining globally robust estimates for the thermophysical properties of intrinsic point defects in crystalline silicon remains challenging. Previous attempts at point-defect model regression have focused on the use of a single type of experimental data but as of yet no single parameter set has produced predictive models for a variety of point-defect related phenomena. A stochastic optimization technique known as simulated annealing is used to perform simultaneous regression of multiple models. Specifically, zinc diffusion in Si wafers and the dynamics of the so-called interstitial-vacancy boundary during Czochralski crystal growth are used to systematically probe point-defect properties. A fully transient model for point-defect dynamics during crystal growth is presented which employs a sophisticated adaptive mesh refinement algorithm to minimize the computational expense associated with each optimization. The resulting framework leads to a quantitatively coherent picture for both experimental systems, which are modeled with a single set of point-defect thermophysical properties. The results are shown to be entirely consistent with other recent model-fitting estimates and indicate that as the number of experiments considered simultaneously within this framework increases it should be possible to systematically specify these properties to higher precision. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0013-4651 |
DOI: | 10.1149/1.1610470 |