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Record W3117709924 · doi:10.1364/ol.412578

Compact silicon-photonic mode-division (de)multiplexer using waveguide-wrapped microdisk resonators

2020· article· en· W3117709924 on OpenAlexafffund
Dusan Gostimirovic, Winnie N. Ye

Bibliographic record

VenueOptics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMultiplexerResonatorFree spectral rangePhotonic integrated circuitOpticsInsertion lossOptoelectronicsPhotonicsMaterials scienceWavelength-division multiplexingSilicon photonicsBandwidth (computing)MultiplexingWaveguideModulation (music)PhysicsWavelengthTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We experimentally demonstrate, to the best of our knowledge, the first microdisk-based silicon-photonic mode-division (de)multiplexer circuit, which is compatible with wavelength-division multiplexing for high aggregate bandwidth on-chip optical communications. This circuit uses waveguide-wrapped microdisk resonators, featuring low levels of intermodal crosstalk and insertion loss within an ultracompact footprint. In addition, the proposed device presents an increased free spectral range, allowing for 530 combined data channels. Furthermore, the microdisk structure naturally supports vertically oriented depletion-type pn junctions, which have been shown to reach subfemtojoule-per-bit modulation efficiencies. The high modulation efficiency, compactness, and wide free spectral range of waveguide-wrapped microdisk resonators present the potential for higher bandwidth and lower energy consumption in next-generation data processing and communication applications.

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 score0.003

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 communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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

Citations20
Published2020
Admission routes2
Has abstractyes

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