Role of fast sputtered particles during sputter deposition: Growth of epitaxial<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Ge</mml:mi></mml:mrow><mml:mrow><mml:mn>0.99</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">C</mml:mi></mml:mrow><mml:mrow><mml:mn>0.01</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo>/</mml:mo><mml:mi mathvariant="normal">G</mml:mi><mml:mi mathvariant="normal">e</mml:mi><mml:mo>(</mml:mo><mml:mn>001</mml:mn><mml:mo>)</mml:mo><mml:mn/></mml:math>
Bibliographic record
Abstract
We show that fast sputtered particles in the sputter-deposition process, largely ignored in previous studies, can play a major role in determining defect densities in as-deposited layers. Epitaxial ${\mathrm{Ge}}_{1\ensuremath{-}y}{\mathrm{C}}_{y}/\mathrm{Ge}(001),$ in which there is a direct correlation between C lattice configurations and the local concentration of Ge self-interstitials, is used as a model materials system. We show that increasing the fraction of fast Ge neutrals in the high-energy tail of the ejected particle distribution increases the concentration of Ge--C split interstitials and thus the film compressive strain. The Ge--C split interstitials form as a result of trapping, by incorporated substitutional C atoms, of Ge self-interstitials produced by incident hyperthermal Ge atoms. Experimental results are supported by Monte Carlo simulations and ab initio calculations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.008 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.935 | 0.011 |
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; both teacher heads agree on what is shown here.
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".