Ion-surface interactions on c-Si(001) at the radiofrequency-powered electrode in low-pressure plasmas: <i>Ex situ</i> spectroscopic ellipsometry and Monte Carlo simulation study
Bibliographic record
Abstract
We use variable-angle spectroscopic ellipsometry (VASE) to investigate oxide and interface formation during plasma-oxidation of monocrystalline Si(001) at the radiofrequency (rf) powered electrode of a plasma-enhanced chemical vapor deposition reactor. HF-etched c-Si(001) wafers were exposed to an oxygen plasma under conditions similar to those used in optical coatings deposition in order to ascertain the effects of plasma-bulk interactions, and to gauge to what depth O2+ and O+ ions interact with and alter the structure and composition of the target in the presence of negative self-bias, VB. From VASE analyses, modifications are best described using a two-layer model: A top layer consisting of SiO2 and a defective interfacial layer (DL) composed of a mixture of c-Si, a-Si, and SiO2. The saturation value of the modification depth (oxide and DL thickness) increases from 3.4±0.4to9.6±0.4nm, for VB ranging from −60to−600V, respectively, and scales with Emax1∕2, where Emax is the maximum energy of ions from an rf discharge. These results are in agreement with nuclear ion-bulk interactions leading to atomic displacements and defect accumulation. The interfacial layer broadens with increasing ∣VB∣ while the fraction of a-Si detected increases from ∼1% up to ∼55% over the investigated VB range, indicative of ballistic and thus depth-dependent oxygen transport to the SiO2–Si interface. Monte Carlo simulations in the binary collision approximation predict significant surface recession due to sputtering, therefore resulting in an apparent self-limiting oxidation mechanism. The surface layers reach their steady-state thicknesses within the first 2min of plasma exposure and subsequently move into the bulk of the c-Si substrate as a result of oxide sputtering and oxygen transport.
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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 | 0.001 |
| 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 | 0.001 | 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".