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Record W2752375150 · doi:10.1364/optica.4.001098

High-speed two-mode switch for mode-division multiplexing optical networks

2017· article· en· W2752375150 on OpenAlexafffund
Yule Xiong, Rubana Bahar Priti, Odile Liboiron-Ladouceur

Bibliographic record

VenueOptica · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOptical burst switchingOptical switchCrossover switchMultiplexingSwitching timeCrossbar switchCircuit switchingMulti-mode optical fiberElectronic engineeringCut-through switchingOptical cross-connectComputer scienceOptical performance monitoringWavelength-division multiplexingBurst switchingTopology (electrical circuits)Transmission (telecommunications)PhysicsOptical fiberEngineeringElectrical engineeringOptoelectronicsTelecommunicationsTransmission delayWavelength

Abstract

fetched live from OpenAlex

Mode-division multiplexing technology using the high-order modes of multimode waveguides enables high-bandwidth data transmission. High-speed mode channel switching is a pivotal function for these optical networks. Here, we propose a modal switching scheme on a silicon-on-insulator platform and demonstrate a high-speed two-mode switch that exploits a Y-junction and multimode interference structure. The design allows for simultaneous switching of two optical modes. A PN-doped junction-based phase shifter in one branch of a Y-junction enables dynamic switching in 2.5 ns. The measured switching extinction ratio is 12.5 dB or better with an open eye diagram for a 10 Gb/s on–off key optical payload signal. The optical power penalty is within 0.5 dB for the two-mode switching at a bit error rate of 10−9. This two-mode switch could enable on-chip mode-based switching network topology for greater aggregated throughput capacity.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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

Citations143
Published2017
Admission routes2
Has abstractyes

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