Donor hyperfine Stark shift and the role of central-cell corrections in tight-binding theory

TL;DR

This study uses atomistic tight-binding simulations to analyze how central-cell corrections, dielectric screening, and lattice strain affect the hyperfine Stark shift of arsenic donors in silicon, achieving close agreement with experimental data.

Contribution

It demonstrates the importance of including non-static dielectric screening and lattice strain in tight-binding models to accurately predict hyperfine Stark shifts.

Findings

Dielectric screening significantly affects the quadratic Stark shift parameter.

Lattice strain further improves the agreement with experimental measurements.

Inclusion of these effects leads to a better understanding of donor wave function behavior.

Abstract

Atomistic tight-binding (TB) simulations are performed to calculate the Stark shift of the hyperfine coupling for a single Arsenic (As) donor in Silicon (Si). The role of the central-cell correction is studied by implementing both the static and the non-static dielectric screenings of the donor potential, and by including the effect of the lattice strain close to the donor site. The dielectric screening of the donor potential tunes the value of the quadratic Stark shift parameter ($\eta_2$) from -1.3 $\times$ 10$^{-3} \mu$m$^2$/V$^2$ for the static dielectric screening to -1.72 $\times$ 10$^{-3} \mu$m$^2$/V$^2$ for the non-static dielectric screening. The effect of lattice strain, implemented by a 3.2% change in the As-Si nearest-neighbour bond length, further shifts the value of $\eta_2$ to -1.87 $\times$ 10$^{-3} \mu$m$^2$/V$^2$, resulting in an excellent agreement of theory with the experimentally measured value of -1.9 $\pm$ 0.2 $\times$ 10$^{-3} \mu$m$^2$/V$^2$. Based on our direct comparison of the calculations with the experiment, we conclude that the previously ignored non-static dielectric screening of the donor potential and the lattice strain significantly influence the donor wave function charge density and thereby leads to a better agreement with the available experimental data sets.