500-period epitaxial Ge/Si0.18Ge0.82 multi-quantum wells on silicon
Bibliographic record
Abstract
Ge/SiGe multi-quantum well heterostructures are highly sought-after for silicon-integrated optoelectronic devices operating in the broad range of the electromagnetic spectrum covering infrared to terahertz wavelengths. However, the epitaxial growth of these heterostructures at a thickness of a few micrometers has been a challenging task due to the lattice mismatch and its associated instabilities resulting from the formation of growth defects. To elucidate these limits, we outline herein a process for the strain-balanced growth on silicon of 11.1/21.5 nm Ge/Si0.18Ge0.82 superlattices (SLs) with a total thickness of 16 μm corresponding to 500 periods. Composition, thickness, and interface width are preserved across the entire SL heterostructure, which is an indication of limited Si–Ge intermixing. High crystallinity and low defect density are obtained in the Ge/Si0.18Ge0.82 layers; however, the dislocation pileup at the interface with the growth substrate induces micrometer-long cracks on the surface. This eventually leads to significant layer tilt in the strain-balanced SL and in the formation of millimeter-long, free-standing flakes. These results confirm the local uniformity of structural properties and highlight the critical importance of threading dislocations in shaping the wafer-level stability of thick multi-quantum well heterostructures required to implement effective silicon-compatible Ge/SiGe 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".