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Record W2104201307 · doi:10.1364/oe.15.010842

Rapid thermal annealing in high repetition rate ultrafast laser waveguide writing in lithium niobate

2007· article· en· W2104201307 on OpenAlexafffund
Amir H. Nejadmalayeri, Peter R. Herman

Bibliographic record

VenueOptics Express · 2007
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLithium niobateMaterials scienceOpticsLaserFluenceFiber laserUltrashort pulseWaveguideAnnealing (glass)OptoelectronicsWavelengthComposite material

Abstract

fetched live from OpenAlex

For the first time to our knowledge, bulk modification of lithium niobate using high repetition rate ultrashort laser pulses has been studied. A fiber based ultrafast laser has been applied in a range of 0.1 to 1.5 MHz repetition rate to directly inscribe optical waveguides in z-cut lithium niobate. Circularly polarized light with stretched 600 fs pulses produced waveguides with nearly circular mode profiles that guided in the telecom band of 1300 nm. Higher laser repetition rate of 700 kHz was found to offer smooth waveguides with low propagation loss of 0.6 dB/cm, matching the best reported value so far, with the advantage of 50 fold faster writing speed. At repetition rates of 250 kHz and higher, the tracks exhibited a cladding-like modification zone that extended outside the main laser interaction volume, yielding smoother structures, despite higher net fluence delivery, providing concrete evidence of heat accumulation and thermal annealing effects. We also present the first observation of periodic micro-structures in the bulk laser interaction volume of a non-glass material.

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 score0.001

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 communication0.0000.000
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 head0.011
GPT teacher head0.226
Teacher spread0.216 · 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

Citations58
Published2007
Admission routes2
Has abstractyes

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