Composition uniformity and large degree of strain relaxation in MBE-grown thick GeSn epitaxial layers, containing 16% Sn
Bibliographic record
Abstract
Abstract We systematically investigate the compositional uniformity, degree of strain relaxation (DSR), defect structure and surface morphology of GeSn epitaxial layers with 16% Sn, grown by low temperature molecular beam epitaxy (MBE) on Ge-buffered Si(001) substrates. Combining atom probe tomography, reciprocal space mapping, cross-sectional transmission electron microscopy, and atomic force microscopy analyses, we demonstrate that for a layer thickness of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>t</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>GeSn</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo>=</mml:mo> <mml:mn>250</mml:mn> <mml:mrow> <mml:mtext> nm</mml:mtext> </mml:mrow> </mml:math> , a high DSR (∼70%) can be achieved, while maintaining compositional uniformity at the atomic scale. We find no evidence of Sn clustering in the bulk, or Sn segregation to the surface, for at least this value of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>t</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>GeSn</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> . The observed compositional uniformity contrasts the well-established phenomenon of strain-relaxation enhancement of Sn content in chemical vapour deposition (CVD) growth of GeSn. The defect structure leading to strain relaxation in these MBE-grown GeSn epitaxial layers is also distinctly different from that observed in CVD growth of the alloy. We observe the co-existence of highly strain-relaxed and pseudomorphically strained regions in the grown epilayers, tentatively explained by bunching of threading dislocations. Considering that MBE growth of GeSn epitaxial layers, with such high-Sn content and layer thickness, has not been reported before, our results are encouraging for future improvements in design and fabrication of group-IV-based mid-infrared photonic devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".