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Record W2795155209 · doi:10.1109/jphot.2018.2819364

Ultra-Broadband Mode Converter and Multiplexer Based on Sub-Wavelength Structures

2018· article· en· W2795155209 on OpenAlexaff
David González‐Andrade, J. Gonzalo Wangüemert‐Pérez, Aitor V. Velasco, Alejandro Ortega‐Moñux, Alaine Herrero-Bermello, Íñigo Molina‐Fernández, Robert Halir, Pavel Cheben

Bibliographic record

VenueIEEE photonics journal · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersEuropean Metrology Programme for Innovation and ResearchMinisterio de Economía y CompetitividadEuropean CommissionComunidad de Madrid
KeywordsMultiplexerMultiplexingBandwidth (computing)BroadbandWavelength-division multiplexingOptical add-drop multiplexerMulti-mode optical fiberPolarization-division multiplexingOptoelectronicsInsertion lossElectronic engineeringOpticsComputer scienceOptical performance monitoringMaterials sciencePhysicsWavelengthTelecommunicationsEngineeringOptical fiber

Abstract

fetched live from OpenAlex

Current bandwidth capacity provided by wavelength-division multiplexing and polarization-division multiplexing is insufficient to keep up with the increasing bandwidth demand required for new services. Mode-division multiplexing technology paves the way to further increase transmission and bandwidth capacities in photonic interconnects. In this work, we propose an ultra-broadband two-mode converter and de/multiplexer based on a sub-wavelength engineered multimode interference coupler, a 90° phase shifter, and a symmetric Y-junction for the silicon-on-insulator platform. Sub-wavelength grating waveguides enable dispersion engineering to further increase the bandwidth operation of conventional multimode interference coupler and, subsequently, of mode de/multiplexer based on them. Full three-dimensional simulations of the designed mode converter and de/multiplexer show insertion loss below than 0.84 dB and crosstalk lower than -20 dB over an unprecedented bandwidth of 300 nm (1.4-1.7 μm). The overall footprint of the proposed device is only 36 μm × 3.7 μm.

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations89
Published2018
Admission routes1
Has abstractyes

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