Spectroscopic ellipsometry on thin titanium oxide layers grown on titanium by plasma oxidation
Bibliographic record
Abstract
Electronic devices based on tunnel junctions require tools able to accurately control the thickness of thin metal and oxide layers on the order of the nanometer. This article shows that multisample ellipsometry is an accurate method to reach this goal on plain uniform layers, in particular for titanium. From these measurements, the authors carefully studied the oxidation rate of titanium thin films in an oxygen plasma. The authors found that the oxide thickness saturates at 5.4±0.4 nm after 10 min in the plasma with an ion acceleration power of 30 W. Increasing this power to 240 W increases the saturation value to 7.6±0.4 nm. An x-ray photoelectron spectroscopy study of the oxide has shown that the oxide created by O2 plasma is stoichiometric (TiO2). The developed model was also used to measure the thicknesses of titanium and titanium oxide layers that have been polished using a chemical mechanical planarization process and a material removal rate of 5.9 nm/min is found with our planarization parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".