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Record W4408602521 · doi:10.1117/12.3050402

Ultrafast laser beam shaping: engineering 3D photonics devices for integration in bulk to fibre glasses

2025· article· en· W4408602521 on OpenAlexaff
Peter R. Herman, Pok Man Chow, Gligor Djogo, Stephen Ho, ­Jun Li­, Yueqi Wang, Polina Zavyalova

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUltrashort pulsePhotonicsMaterials scienceLaser beamsLaserUltrafast opticsOptoelectronicsFiber laserBeam (structure)OpticsEngineering physicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Adaptive Optics (AO) is facilitating new forms of laser beam shaping from which novel forms of three-dimensional (3D) nano-structures may be tailored inside of transparent material by ultrashort-pulsed laser interaction. The assembly of such tailored nano-diffractive elements into micro-optic diffractive optics presents new opportunities for photonics integration and packaging that encompasses 3D processing over a broad range of applications based inside of bulk glasses, thin transparent films, and optical fibre. The presentation explores opportunities for integrating multi-functional photonic and opto-fluidic devices into a compact platform of lab-in-fiber (LIF) or fiber cladding photonics that bypass requirements for integration of planar waveguide circuits with fiber optic networks. Alternatively, the 3D laser nano-structuring promises new means for fiber-to-chip packaging with 3D waveguide circuits, for example, the formation of low profile interposers that permit low-loss grating coupling of optical fibers to silicon photonic chips.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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 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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