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Record W2072586674 · doi:10.1117/12.474335

Rugate filters grown by Glancing Angle Deposition

2003· article· en· W2072586674 on OpenAlexafffund
Kate Kaminska, Tim Brown, Gisia Beydaghyan, Kevin Robbie

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceRefractive indexOptical filterFabricationOpticsNanometreDeposition (geology)Optical coatingSubstrate (aquarium)Thin filmPhysical vapor depositionFilter (signal processing)OptoelectronicsInterference filterSiliconEvaporationComputer scienceNanotechnologyComposite materialPhysics

Abstract

fetched live from OpenAlex

Recent progress in thin film optical coating technology has enabled more complex filter designs and better control of out of band interference. One of the most significant advances in optical filters has been the manufacture of rugate filter designs based on sinusoidal variation of refractive index. The realization of a rugate filter requires a means of depositing an optical material whose refractive index can be significantly varied over a wide range, while having precise control of the index. The Glancing Angle Deposition (GLAD) technique satisfies these requirements by allowing fabrication of films with nano-engineered morphology whose optical properties can be tailored. GLAD is based on thin film physical vapor deposition by evaporation and employs oblique angle flux and substrate motion to allow nanometer scale control of structure and optical properties. Silicon rugate filter prototypes were made according to design specifications using computer control of deposition parameters which influence the film optical response.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.216
Teacher spread0.208 · 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.

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

Citations7
Published2003
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coatings and GratingsFrench-language works237,207