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Record W2907529747 · doi:10.1364/oe.27.000166

Experimental quantification of the free-carrier effect in silicon waveguides at extended wavelengths

2019· article· en· W2907529747 on OpenAlexaff
David E. Hagan, Miloš Nedeljković, Wei Cao, David J. Thomson, Goran Z. Mashanovich, Andrew P. Knights

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceWavelengthOpticsAttenuationOptoelectronicsAttenuator (electronics)RangingOptical amplifierOptical attenuatorResonatorWavelength-division multiplexingOptical fiberMulti-mode optical fiberLaserPhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We examine the electro-optic effect at wavelengths ranging from 1.31 to 2.02 μm for: (1) an Electronic Variable Optical Attenuator (EVOA); and (2) a Micro-Ring Resonator (MRR). For the EVOA, simulations were performed to ascertain the relationship between free-carrier concentration and optical attenuation, and are in agreement with our observation of an increase in attenuation with increasing wavelength. MRRs were fabricated for use around wavelengths of 2 μm to explore the sensitivity of operation to bus-to-ring coupling gap and p-n junction offset. Trends observed in the experiment are replicated by simulation, calibrated using the observations of the EVOA operation. The previously proposed efficiency increase of operation around 2 μm compared to more traditional wavelengths is demonstrated. Future development of devices for these wavelengths, supported by amplification using Thulium Doped Fiber Amplifier (TDFA) technology, is a promising route to aid in the alleviation of increasing demands on communication networks.

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 score0.005

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 studies0.0000.001
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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