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Record W1986172750 · doi:10.1364/ao.45.008298

Oxidation of evaporated porous silicon rugate filters

2006· article· en· W1986172750 on OpenAlexaff
Kevin Robbie, Yan Cui, Chelsea Elliott, Kate Kaminska

Bibliographic record

VenueApplied Optics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials sciencePorous siliconRefractive indexSiliconTransmittanceNanoporousOpticsOptical filterOptoelectronicsHigh-refractive-index polymerNanotechnology

Abstract

fetched live from OpenAlex

Rugate filters are thin-film optical interference coatings with sinusoidal variation of the refractive index. Several of these filters were fabricated with glancing angle deposition, which exploits atomic competition during growth to create nanoporous materials with controllable effective refractive index. This method enables the fabrication of devices with almost arbitrary refractive index profiles varying between the ambient, 1.0, and the index of the film material, in this case silicon with an index of 4.0 (at 600 nm). As these filters are inherently porous, oxidation of the silicon can occur throughout the device layer, and here we study the intentional oxidation of silicon filters by high-temperature reaction with gaseous oxygen. We find that a significant portion of the silicon filter oxidizes in approximately 10 min when heated to 600 degrees C-650 degrees C in an oxygen environment; oxidation then continues slowly over several hours. The presence of water vapor has little apparent effect on the oxidation reaction, and attempts to oxidize with ozone at room temperature were unsuccessful. As silicon filters oxidize to become silica, spectral blueshifts and increased short-wavelength transmittance are observed. Measured and calculated transmittance spectra generally agree, although the lack of absorption and dispersion in the theoretical model limits detailed comparison.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.212
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2006
Admission routes1
Has abstractyes

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