Evidence of sub-10 nm aluminum-oxygen precipitates in silicon
Bibliographic record
Abstract
In this research, ultraviolet laser-assisted atom-probe tomography (APT) was utilized to investigate precisely the behavior at the atomistic level of aluminum impurities in ultrathin epitaxial silicon layers. Aluminum atoms were incorporated in situ during the growth process. The measured average aluminum concentration in the grown layers exceeds by several orders of magnitude the equilibrium bulk solubility. Three-dimensional atom-by-atom mapping demonstrates that aluminum atoms precipitate in the silicon matrix and form nanoscopic precipitates with lateral dimensions in the 1.3 to 6.2 nm range. These precipitates were found to form only in the presence of oxygen impurity atoms, thus providing clear evidence of the longhypothesized role of oxygen and aluminum-oxygen complexes in facilitating the precipitation of aluminum in a silicon lattice. The measured average aluminum and oxygen concentrations in the precipitates are ∼10 ± 0.5 at.% and ∼4.4 ± 0.5 at.%, respectively. This synergistic interaction is supported by first-principles calculations of the binding energies of aluminum-oxygen dimers in silicon. The calculations demonstrate that there is a strong binding between aluminum and oxygen atoms, with Al-O-Al and O-Al-Al as the energetically favorable sequences corresponding to precipitates in which the concentration of aluminum is twice as large as the oxygen concentration in agreement with APT data.
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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".