Using Data to Enhance Mechanistic Modeling of Microstructure Evolution in Silicon

ORAL · Invited

Abstract

Mechanistic models of microstructural evolution in silicon-based materials for electronic and energy applications have been used widely in a variety of settings. Examples include point defect transport and aggregation during bulk silicon crystal growth and wafer processing, surface microstructure evolution during deposition, and impurity segregation at line and planar defects. While much of the physics underpinning these phenomena are well established in principle, uncertainties are often present in the form of unspecified thermophysical model parameters or incomplete descriptions of the physics, especially at the atomistic level. In many cases, these uncertainties can be resolved using data, either measured experimentally or computed using supporting simulations.

In this talk, two examples of data-assisted mechanistic modeling are presented to illustrate how data may be used to ‘patch over’ modeling elements that are not fully specified. In the first example, a continuum model of oxide precipitation in Czochralski-grown Si crystals is assisted using experimental measurements of precipitate density [1]. While the basic features of oxide precipitation in Si are well-understood, a complete atomistic description of the process is still lacking. In this approach, the model is used to bridge the unknown atomistic properties and the highly coarse-grained experimental measurements. In the second example, we consider the deposition of Ge on an amorphous silica substrate. Here, experimental data is used to first refine an atomistic potential. Then, we use analytical models to make connections between the vast amounts of data generated by the atomistic simulations and experimentally meaningful quantities [2,3].

[1] Y. Yang, A. Sattler, and T. Sinno, Data-Assisted Physical Modeling of Oxygen Precipitation in Silicon Wafers, J. Appl. Phys. 125 (2019), 165705.

[2] C. Y. Chuang, S. M. Han, L. A. Zepeda-Ruiz, and T. Sinno, On Coarse Projective Integration for Atomic Deposition in Amorphous Systems, J. Chem. Phys. 143 (2015) 134703.

[3] C. Y. Chuang, Q. Li, D. Leonhardt, S. M. Han, and T. Sinno, Atomistic Analysis of Germanium on Amorphous SiO2 using an Empirical Interatomic Potential, Surf. Sci. 609 (2013) 221.

Publication: [1] Y. Yang, A. Sattler, and T. Sinno, Data-Assisted Physical Modeling of Oxygen Precipitation in Silicon Wafers, J. Appl. Phys. 125 (2019), 165705.
[2] C. Y. Chuang, S. M. Han, L. A. Zepeda-Ruiz, and T. Sinno, On Coarse Projective Integration for Atomic Deposition in Amorphous Systems, J. Chem. Phys. 143 (2015) 134703.
[3] C. Y. Chuang, Q. Li, D. Leonhardt, S. M. Han, and T. Sinno, Atomistic Analysis of Germanium on Amorphous SiO2 using an Empirical Interatomic Potential, Surf. Sci. 609 (2013) 221.

Presenters

  • Talid Sinno

    University of Pennsylvania

Authors

  • Talid Sinno

    University of Pennsylvania