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Record W4408970819 · doi:10.1109/jlt.2025.3555793

Mode-Selective Photonic Lanterns With Double-Clad Fibers

2025· article· en· W4408970819 on OpenAlexaff
Rodrigo Itzamná Becerra-Deana, Martin Poinsinet de Sivry-Houle, Stéphane Virally, Caroline Boudoux, Nicolas Godbout

Bibliographic record

VenueJournal of Lightwave Technology · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsOptical fiberPhotonicsMaterials scienceOptoelectronicsPhotonic-crystal fiberOpticsMode (computer interface)Computer sciencePhysics

Abstract

fetched live from OpenAlex

We present the design, fabrication, and characterization of mode-selective photonic lanterns using double-clad fibers. Here, we exploited several custom-pulled double-clad fibers to achieve the symmetry break required to excite higher-order modes. The resulting components are short and exhibit high modal isolation and low excess loss. They address some of the limitations of existing photonic lanterns in terms of fragility and coupling efficiency. The fabrication process involves the use of lower-index capillary tubes to maintain fiber geometry during fusion and tapering. Through the use of varying first cladding diameters, mode selectivity is achieved without sacrificing single-mode compatibility. This in turn allows proper real-time characterization during the whole fabrication process. Results demonstrate that double-clad fibers stacked inside a fluorine-doped capillary tube feature high modal isolation (above 60 dB) and low excess loss (lower than 0.49 dB), over a broad wavelength range (more than 250 nm) with steeper taper profiles, and more robust components. The use of less expensive synthetic fused silica capillary tubes achieves high modal isolation (above 20 dB) and excess loss lower than 2 dB over the same broad wavelength range.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.264
Teacher spread0.259 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2025
Admission routes1
Has abstractyes

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