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Record W2975573360 · doi:10.1109/jphot.2019.2942973

Zero-Birefringence Silicon Waveguides Based on Tilted Subwavelength Metamaterials

2019· article· en· W2975573360 on OpenAlexaff
Alaine Herrero-Bermello, José Manuel Luque‐González, Robert Halir, Pavel Cheben, Alejandro Ortega‐Moñux, Íñigo Molina‐Fernández, Aitor V. Velasco

Bibliographic record

VenueIEEE photonics journal · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersComunidad de Madrid
KeywordsBirefringenceMetamaterialMaterials scienceOptoelectronicsOpticsSiliconSilicon photonicsZero (linguistics)Physics

Abstract

fetched live from OpenAlex

Polarization independent silicon-on-insulator nanowires are highly sought after, due the inherent high birefringence of this material platform. State-of-the-art designs of non-birefringent waveguides include ridge waveguides and square nanowires, which either imply large dimensions, multiple etching steps, low fabrication tolerances or high wavelength dependence. In this work, we overcome all the aforementioned limitations through tilted subwavelength structures which provide anisotropy control of the resulting metamaterial. With a waveguide cross section of only 300 nm x 550 nm (height x width), the zero-birefringence point is obtained for an approximately 48°-tilt of the subwavelength structure. Birefringence of the nominal design deteriorates by only 9.10-3even in the presence of size deviations of +10 nm. Moreover, birefringence is maintained under 6.10-3in a 100-nm bandwidth around the central wavelength of 1550 nm. This innovative approach is readly adaptable to a wide range of waveguide sizes, while maintaining single-etch-step fabrication.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.226
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2019
Admission routes1
Has abstractyes

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