Composition and growth of anodic and thermal oxides on InP and GaAs
Bibliographic record
Abstract
Abstract Research using surface analytical techniques (Auger, XPS and 16 O/ 18 O SIMS) to characterize anodic films (formed in aqueous electrolytes at ambient temperature) and thermal oxides (formed at ∼500°C) on both InP and GaAs is summarized. Anodic and thermal oxides on InP comprise an outer indium‐rich layer and an inner layer containing In 2 O 3 , phosphorus oxide and P–O compounds with indium. For anodic films, sequential 16 O/ 18 O SIMS experiments indicate oxygen ion ingress with inner layer growth at the film/substrate interface and outer layer growth of In 2 O 3 at the film/electrolyte interface. Electrical measurements performed on metal–insulator–semiconductor (MIS) structures indicate the oxides on InP to be ‘leaky’. Thermal oxides formed on GaAs in oxygen consist primarily of Ga 2 O 3 with a small amount of arsenic (a few per cent) at the outer oxide surface as both As 2 O 3 and As 2 O 5 and a significant accumulation of elemental arsenic at the oxide/substrate interface. This probably leads to a high density of electronic traps and poor electrical properties. Anodic oxides (∼40 nm thick) formed in phosphate solution, however, have better electrical properties exhibiting low current densities (up to 6 V), making these films potentially useful for device applications. Copyright © 2002 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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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".