In Situ Deposition Monitoring by a Tilted Fiber Bragg Grating Optical Probe: Probing Nucleation in Chemical Vapour Deposition of Gold
Bibliographic record
Abstract
The gold(I) compound [Au(NiPr)2CNMe2]2 was used as a chemical vapour deposition precursor to deposit gold metal films between 200 – 225 °C on both flat silicon substrates as well as on silica optical fibers. The deposited gold film was nanocrystalline with a preferred (111) orientation and had marginal conductivity. The film shows good purity and has a very high growth rate of 222 nm/min (37 Å/s) when measured on the optical fiber. The growth of the gold film on the optical fiber was monitored by the attenuation of a guided light mode diffracted into the fiber's cladding by a tilted Bragg grating (TFBG) inscribed in the fiber core. The initiation of the gold film growth was found to coincide with an increase in the attenuation of polarized light propagating in the fiber and monitored at wavelengths near 1559 nm. This attenuation peaked when the film granularity maximized losses to the cladding mode and then disappeared when the film became thicker and continuous. Further evidence of a continuous film was indicated by a wavelength shift of 0.2 nm of the cladding mode resonance wavelengths. A growth rate of 222 nm/min (37 Å/s) was found during the first 50 seconds of deposition, tapering off to an overall growth rate of 39 nm/min (6.5 Å/s). The use of a TFBG as a sensor was shown to be a valuable method to monitor film nucleation, growth and uniformity.
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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 | 0.001 | 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".