Surface Second Harmonic Generation as a Probe of Anodic Oxidation of Si[001]
Bibliographic record
Abstract
SUMMARY Anodic oxidation of Si(OO1) has been well studied by traditional electrochemical techniques.' A detailed description of the process is however lacking due to insufficient knowledge of tlhe state of the silicon surface during the course of the reaction. In the present work, we emploly surface second harmonic generation (SHG) to monitor the oxidation process in situ. Our recent work has clearly shown that the variation of the SHG signal during oxidation is largely determined by the evolution of the space charge field in the silicon. This result is consistent with the experiments of previous workers that demonstrate that thie nonlinear optical response of SUSi02 interfaces is sensitive to the presence of electric fields in the space charge region (SCR) of' By enabling us to monitor the surface potential in the Si during oxide growth, these in situ optical experiments reveal details of the oxidation mechanism that are difficult or impossible to study by using purely electrochemical methods. The experimental arrangement for in situ monitoring of anodic oxidation by surface SHG is shown in Figure 1. Anodic oxide growths were carried out in a three electrode electrochemical cell containing 0.1 M HCI electrolyte. The potential between the p-doped (7 x: 1014 ~m-~) Si(OO1) wafer and a saturated calomel electrode (SCE) was varied by using a computer controlled potentiostat. Prior to each experiment, the Si wafer was etched in 1% HF to remove the existing oxide and leave a H-terminated surface.
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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".