MétaCan
Menu
Back to cohort
Record W2975547194 · doi:10.1002/lpor.201900172

An Ultracompact GRIN‐Lens‐Based Spot Size Converter using Subwavelength Grating Metamaterials

2019· article· en· W2975547194 on OpenAlexaff
José Manuel Luque‐González, Robert Halir, J. Gonzalo Wangüemert‐Pérez, J. de‐Oliva‐Rubio, Jens H. Schmid, Pavel Cheben, Íñigo Molina‐Fernández, Alejandro Ortega‐Moñux

Bibliographic record

VenueLaser & Photonics Review · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersEuropean Regional Development FundMinisterio de Economía y CompetitividadMinisterio de Educación, Cultura y DeporteUniversidad de Málaga
KeywordsOpticsMetamaterialGratingMaterials scienceFinite-difference time-domain methodPlanarBandwidth (computing)OptoelectronicsWaveguidePhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Abstract Graded‐index materials offer virtually complete control over light propagation in integrated photonic chips but can be challenging to implement. Here, an anisotropic graded‐index metamaterial, synthesized with fully etched silicon subwavelength structures, is proposed. Based on this material, a spot size converter that expands the transverse electric (TE) mode field profile from a 0.5 µm wide silicon wire waveguide to a 15 µm wide waveguide within a length of only 14 µm is designed. Measured insertion losses are below 1 dB in an unprecedented 130 nm bandwidth, limited by the measurement setup, with full 3D finite‐difference time‐domain (FDTD) simulations predicting a bandwidth in excess of 300 nm. Furthermore, the device is well suited to feed fiber‐to‐chip grating couplers, while requiring a footprint ten times smaller than conventional adiabatic tapers.

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 score0.000
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)0.0000.000

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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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

Citations62
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueLaser & Photonics ReviewSame topicPhotonic and Optical DevicesFrench-language works237,207