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Record W2089066478 · doi:10.1117/12.590850

MHz-rate ultrafast fiber laser for writing of optical waveguides in silica glasses

2005· article· en· W2089066478 on OpenAlexaff
Lawrence Shah, Fumiyo Yoshino, Alan Arai, Shane M. Eaton, Haibin Zhang, Stephen Ho, Peter R. Herman

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLaserMaterials scienceFemtosecondOpticsUltrashort pulseFiber laserWaveguideOptoelectronicsBorosilicate glassRepetition (rhetorical device)Optical fiberPhysics

Abstract

fetched live from OpenAlex

Direct waveguide writing with femtosecond lasers can be divided into two general categories based upon the type of lasers used: amplified systems that emit high pulse energy (>2 &mu;J) at low repetition rates (<250 kHz), and oscillators that produce low energy (<200 nJ) at high repetition rates (>1 MHz). In this presentation, we report on waveguide writing with a novel commercial femtosecond fiber laser system (IMRA, FCPA &mu;Jewel) that bridges the gap between these two regimes, providing sub-400 fs pulses with pulse energies of >2.5 &mu;J at 100 kHz and >150 nJ at 5 MHz. The laser repetition rate can be varied from 100 kHz to 5 MHz in 1 kHz increments through a computer controlled user interface. The ability to quickly and easily vary the repetition rate of this laser was critical in identifying and optimizing laser processing windows for different target glasses. An overview of laser processing windows and waveguide characteristics are presented for borosilicate and fused silica glasses exposed to fundamental (1045 nm) and second harmonic (522 nm) laser light.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.235
Teacher spread0.224 · 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.

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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicLaser Material Processing TechniquesFrench-language works237,207