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Record W4408706912 · doi:10.1117/12.3058157

Advancements in GaN DFBs with embedded gratings and a path to higher power

2025· article· en· W4408706912 on OpenAlexaff
Ryan M. Anderson, SeungGeun Lee, Emily Trageser, J.D. Brown, Amy Zhang, Bin Zhang, Carol Huang, Tanner Massimino, Qian Gao, Alanna Fernandes, C. J. Pinzone, Brad Siskavich, I. R. Mann, Steven P. DenBaars, Shuji Nakamura, Jim Haden

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsDouglas College
Fundersnot available
KeywordsPath (computing)Power (physics)Computer sciencePhysicsComputer network

Abstract

fetched live from OpenAlex

GaN-based distributed feedback (DFB) laser diodes are narrow linewidth sources promising for integration into low-size, weight, and power photonic circuits. There is a need to improve the linewidth, expand the availability of technologically useful wavelengths, and increase the power and efficiency for several applications. BluGlass presents advancements in visible wavelength DFB lasers. We will show measured device results achieving higher side-mode-suppression ratio of 40dB and a peak full-width half maximum under 3pm demonstrating near single frequency emission. Devices targeting critical atomic transition wavelengths for clocks, quantum computing, and other cold physics applications are covered spanning from 408nm to 470nm. A path toward narrow-band high-power DFB sources will be presented with preliminary data on gain in GaN-based semiconductor optical amplifiers.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.249
Teacher spread0.243 · 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
Published2025
Admission routes1
Has abstractyes

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Same topicGaN-based semiconductor devices and materialsFrench-language works237,207