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Record W2596255523 · doi:10.1364/ol.42.001181

High-power thulium lasers on a silicon photonics platform

2017· article· en· W2596255523 on OpenAlexaff
Nanxi Li, Purnawirman, Zhan Su, Emir Salih Magden, Patrick T. Callahan, Katia Shtyrkova, Ming Xin, Alfonso Ruocco, Christopher Baiocco, Erich P. Ippen, Franz X. Kärtner, Jonathan D. B. Bradley, Diedrik Vermeulen, Michael R. Watts

Bibliographic record

VenueOptics Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster University
FundersNational Defense Science and Engineering GraduateAdvanced Research Projects AgencyDefense Advanced Research Projects Agency
KeywordsMaterials scienceLaserOpticsOptoelectronicsDistributed Bragg reflector laserPhotonicsThuliumTunable laserDistributed Bragg reflectorSiliconSilicon photonicsFiber laserHybrid silicon laserWavelengthDopingPhysics

Abstract

fetched live from OpenAlex

Mid-infrared laser sources are of great interest for various applications, including light detection and ranging, spectroscopy, communication, trace-gas detection, and medical sensing. Silicon photonics is a promising platform that enables these applications to be integrated on a single chip with low cost and compact size. Silicon-based high-power lasers have been demonstrated at 1.55 μm wavelength, while in the 2 μm region, to the best of our knowledge, high-power, high-efficiency, and monolithic light sources have been minimally investigated. In this Letter, we report on high-power CMOS-compatible thulium-doped distributed feedback and distributed Bragg reflector lasers with single-mode output powers up to 267 and 387 mW, and slope efficiencies of 14% and 23%, respectively. More than 70 dB side-mode suppression ratio is achieved for both lasers. This work extends the applicability of silicon photonic microsystems in the 2 μm region.

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations70
Published2017
Admission routes1
Has abstractyes

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