Enhanced Sn incorporation in GeSn epitaxial semiconductors via strain relaxation
Bibliographic record
Abstract
We investigate the effect of strain on the morphology and composition of GeSn layers grown on Ge/Si virtual substrates. By using buffer layers with controlled thickness and Sn content, we demonstrate that the lattice parameter can be tuned to reduce the strain in the growing top layer (TL) leading to the incorporation of Sn up to 18 at. %. For a 7 at. % bottom layer (BL) and a 11-13 at. % middle layer (ML), the optimal total thickness tGeSn = 250-400 nm provides a large degree of strain relaxation without apparent nucleation of dislocations in the TL, while incorporating Sn at concentrations of 15 at. % and higher. Besides facilitating the growth of Sn-rich GeSn, the engineering of the lattice parameter also suppresses the gradient in Sn content in the TL, yielding a uniform composition. We correlate the formation of the surface cross-hatch pattern with the critical thickness hG for the nucleation and gliding of misfit dislocations at the GeSn-Ge interface that originate from gliding of pre-existing threading dislocations in the substrate. When the GeSn layer thickness raises above a second critical thickness hN, multiple interactions between dislocations take place, leading to a more extended defective ML/BL, thus promoting additional strain relaxation and reduces the compositional gradient in the ML. From these studies, we infer that the growth rate and the Ge-hydride precursors seem to have a limited influence on the growth kinetics, while lowering temperature and enhancing strain relaxation are central in controlling the composition of GeSn. These results contribute to the fundamental understanding of the growth of metastable, Sn-containing group-IV semiconductors, which is crucial to improve the fabrication and design of silicon-compatible mid-infrared photonic devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".