In Situ Studies of Germanium-Tin and Silicon-Germanium-Tin Thermal Stability
Bibliographic record
Abstract
Sn-containing group IV semiconductors provide a rich playground to independently engineer the band structure and lattice parameter with a potential impact of a variety of silicon-based electronic and optoelectronic devices. The introduction of these metastable alloys in device fabrication raises a number of concerns regarding the possible degradation of their composition and structural properties during different processing steps. With this perspective, in this work we present detailed in situ and ex situ investigations of the thermal behavior of both Sn-rich binary and ternary alloys. We used low energy electron microscopy and photoelectron emission electron microscopy to examine in real time the evolution of surface structure and composition during thermal annealing. These in situ studies are augmented using several ex situ characterization techniques. These investigations unraveled unprecedented details about the phase separation in these two systems. Particularly, in Ge0.84Si0.04Sn0.12 annealing above 410 °C leads to the formation of randomly distributed Sn-rich particles which grow as the annealing temperature increases. Additionally, the binary alloy Ge0.88Sn0.12 seems to be relatively more stable as compared to the ternary alloy with the same Sn content. The Sn-rich particles in the former system are not randomly distributed, but they are found to follow a well defined pattern on the surface along the <110> direction. The mechanisms and regimes involved in the phase separation are also briefly presented.
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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".