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Record W4386128257 · doi:10.1109/pn58661.2023.10223047

Integrated distributed Bragg reflector lasers in silicon nitride waveguides coated with erbium-doped tellurite

2023· article· en· W4386128257 on OpenAlexafffund
Bruno L. Segat Frare, Pooya Torab Ahmadi, Batoul Hashemi, Henry C. Frankis, Peter Mascher, Jonathan D. B. Bradley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceDistributed Bragg reflectorOptoelectronicsLasing thresholdErbiumLaserSiliconSilicon nitrideDistributed Bragg reflector laserDopingOpticsNitrideReflector (photography)NanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

In this work, we show on-chip distributed Bragg reflector lasers in 200-nm-thick silicon nitride waveguides coated with erbium-doped tellurium dioxide. The devices consist of asymmetrical cavities defined by sidewall corrugations patterned directly on 2.2-cm-long silicon nitride waveguides. The lasers operate within the C-band (1530-1565 nm) with highly directional output and efficiencies up to 0.36% and lasing threshold as low as 13 mW when pumped with 1470 nm light.

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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