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Record W2896861499 · doi:10.2351/1.5065748

Writing buried optical waveguides: Contrasts in ultrafast and ultraviolet lasers

2002· article· en· W2896861499 on OpenAlexaff
Peter R. Herman, Midori X. Wei, Dragan Ćorić, ­Jun Li­, Amir H. Nejadmalayeri, P. B. Corkum, V. R. Bhardwaj, D. M. Rayner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsSteacie Institute for Molecular SciencesUniversity of Toronto
Fundersnot available
KeywordsLaserMaterials scienceUltrashort pulseOpticsRefractive indexPhotonicsOptoelectronicsUltravioletSapphireAbsorption (acoustics)Physics

Abstract

fetched live from OpenAlex

Ultrafast lasers and deep-ultraviolet F2 lasers present stark contrasts in driving multi-photon and single-photon interactions within transparent glasses. Low-loss single mode waveguides and simple photonic components were formed with a 50-fs Ti:Sapphire laser and a 157-nm 15-ns F2 laser inside the bulk of fused silica. Laser interactions were confined by large NA optics within a small ∼8-μm diameter focal volume and scanned with precision motion stages to shape 3-dimensional photonic structures. Refractive index changes of up to 0.01 were induced with the ultrafast laser at a high scanning speed of several hundred microns per second and 100-kHz repetition rate. Weaker refractive index changes of 0.0005 were generated by the F2 laser, owing to a lower 100-Hz repetition rate and weaker absorption in the focal volume. The laser-generated waveguides offer high transmission of ∼1 dB/cm, are transparent to light in the visible and infrared spectrum, and can support multi-mode guiding with low insertion loss into optical fibers. The paper will present examples of fabricating simple three-dimensional photonic components.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.499

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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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