(608e) Mesoscale Modeling of Stress-Directed Compositional Patterning in Semiconductor Alloys

The ability to scalably fabricate periodic, large-area assemblies of Ge quantum dots (QDs) on Si or SiGe substrates with high degree of spatial and size uniformity would impact numerous technologies, including nano-/micro-electronics, optoelectronics, nanosensor arrays, and high-density patterned media for data storage. In this talk, we study computationally a process in which SiGe substrates are compositionally patterned over large areas using spatially-modulated elastic fields applied by a nano-indenter array [1]. In this process, an indenter array is pressed against a SiGe wafer and the entire assembly is annealed at high temperatures during which the larger Ge atoms are selectively driven away from areas of compressive stress. Compositional analysis of the substrates demonstrates that this approach leads to a transfer of the indenter array pattern into the near-surface compositional distribution.

We describe here a mesoscopic model of the â??stress transferâ?? process. The model is based on a multiresolution lattice kinetic Monte Carlo (LKMC) simulation that is propagated using rates for atomic diffusion that depend explicitly on local values of stress, composition, and temperature [2]. The dependence of atomic diffusion on composition is regressed to experimental data while the stress dependence is described using the theory of activation volumes [3]. The stress field is computed using a continuum linear elastic description of the contact problem and is updated quasi-statically as the composition in the SiGe substrate evolves.

The model is used to investigate systematically the impact of several process parameters, including the shape of the indenters, the spacing between indenters, the annealing temperature, and the indenter strength. We find that certain process parameter configurations lead to compositional structures that may be useful for quantum confinement. Finally, the model performance is discussed, along with recent enhancements that lead to large increases in computational efficiency.

[1] S. Ghosh, D. Kaiser, J. Bonilla, T. Sinno and S. M. Han, Appl. Phys. Lett. 107 072106 (2015).

[2] D. Kaiser, S. Ghosh, S. M. Han and T. Sinno, Mol. Systems Des. & Eng. DOI:10.1039/C6ME00017G (2016).

[3] M. J. Aziz, Applied Physics Letters 70, 2810 (1997).