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Record W3201857729 · doi:10.1109/lpt.2021.3116439

Mode Converter and Multiplexer With a Subwavelength Phase Shifter for Extended Broadband Operation

2021· article· en· W3201857729 on OpenAlexaff
David González‐Andrade, Raquel Fernández de Cabo, Jaime Vilas, Irene Olivares, A. Días, José Manuel Luque‐González, J. Gonzalo Wangüemert‐Pérez, Alejandro Ortega‐Moñux, Íñigo Molina‐Fernández, Robert Halir, Pavel Cheben, Aitor V. Velasco

Bibliographic record

VenueIEEE Photonics Technology Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersMinisterio de Ciencia e InnovaciónCentro para el Desarrollo Tecnológico IndustrialComunidad de Madrid
KeywordsDemultiplexerMultiplexerPhase shift moduleInsertion lossMaterials scienceSilicon on insulatorOptoelectronicsBroadbandOpticsOptical switchMultiplexingElectronic engineeringSiliconPhysicsEngineering

Abstract

fetched live from OpenAlex

On-chip mode converters and multiplexers are fundamental components to scale the capacity of silicon optical interconnects by using different spatial modes of waveguides. Recently, we proposed a low loss and compact mode converter and multiplexer consisting of a subwavelength-engineered multimode interference coupler, tapered waveguides as phase shifter and a symmetric Y-junction. However, the narrow spectral response of the tapered phase shifter limited the device crosstalk performance. In this work, we demonstrate that the use of a subwavelength grating phase shifter with low phase-shift errors substantially reduces the crosstalk and expands the operational bandwidth. A complete multiplexer-demultiplexer link consisting of two devices in back-to-back configuration was fabricated in a 220-nm silicon-on-insulator platform. Experimental measurements of the complete link show insertion loss below 2 dB and crosstalk less than −17 dB over a bandwidth of 245 nm (1427 – 1672 nm).

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 score0.494
Threshold uncertainty score0.758

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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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