Evidence for vacancy trapping in Au-hyperdoped Si following pulsed laser melting
Bibliographic record
Abstract
Nanosecond pulsed laser melting can be used to rapidly recrystallize ion-implanted Si through liquid phase epitaxy. The rapid resolidification that follows the melting results in a supersaturation of impurities and hyperdopes the Si, inducing novel optoelectronic properties with a wide range of applications. In this work, structural changes in the Si lattice in Au-hyperdoped Si are studied in detail. Specifically, we show that the local skewing of the lattice observed previously in regions of extremely high Au concentrations (>1.4 at. %) can be related to the displacement of Au from perfect lattice positions. Surprisingly, although the incorporation of the larger Au atoms into Si is expected to cause swelling of the lattice, reciprocal space mapping shows that a small amount (0.3 at. %) of lattice contraction (decrease in lattice parameter) is present in the hyperdoped layer. Furthermore, positron annihilation spectroscopy shows an elevated concentration of vacancies in the hyperdoped layer. Based on these observations and with the aid of density functional theory, we propose a phenomenological model in which vacancies are kinetically trapped into lattice sites around substitutional Au atoms during resolidification. This vacancy trapping process is hypothesized to occur as a means to minimize lattice strain and may be universal in pulsed laser melted Si systems.
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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".