4.7 Article

Reduced Dislocation Introduction in III-V/Si Heterostructures with Glide-Enhancing Compressively Strained Superlattices

Journal

CRYSTAL GROWTH & DESIGN
Volume 20, Issue 10, Pages 6939-6946

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.cgd.0c00992

Keywords

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Funding

  1. National Science Foundation [1708957]
  2. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE) [DE-EE0007539]
  3. NASA Space Technology Research Fellowship [NNX16AM50H]
  4. National Science Foundation Graduate Research Fellowships [DGE-1343012, DGE-1650114]
  5. Division Of Materials Research
  6. Direct For Mathematical & Physical Scien [1708957] Funding Source: National Science Foundation

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The novel use of a GaAsyP1-y/GaP compressively strained superlattice (CSS) to provide enhanced control over misfit dislocation (MD) evolution and threading dislocation density (TDD) during GaP/Si metamorphic heteroepitaxy is demonstrated. Insertion of the CSS just after critical thickness, and thus prior to substantial dislocation introduction, is found to yield significantly reduced TDD in relaxed, 500 nm thick, n-type GaP/Si versus comparable control samples. The impact of CSS period count on average TDD and the overall dislocation network morphology was examined, supported by quantitative microstructural characterization, revealing a nearly 20x relative TDD reduction (to (2.4 +/- 0.4) x 10(6) cm(-2)) with a 3-period CSS structure. A similarly low TDD ((3.0 +/- 0.6) x 10(6) cm(-2)) is maintained when the resultant n-GaP/Si virtual substrate is used for the growth of a subsequent n-type GaAs0.75P0.25-terminal GaAsyP1-y step-graded metamorphic buffer. Although the physical mechanism for TDD reduction provided by these structures is not yet entirely understood, this initial work suggests that enhanced glide dynamics of MDs at or within the CSS placed early in the growth leads to a reduction in the total number of dislocations introduced overall, as opposed to annihilation-based reduction that occurs in conventional strained-layer superlattice dislocation filter approaches.

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