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 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.