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Record W4255301392 · doi:10.21203/rs.3.rs-578919/v1

A Systematic Study of PPLN Length Dependence in Intracavity SHG

2021· preprint· en· W4255301392 on OpenAlexafffund
Tyler Kashak, Liam Flannigan, Chang‐Qing Xu

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPhotorefractive and Nonlinear Optics
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFull width at half maximumSecond-harmonic generationMaterials scienceLithium niobateOpticsSaturation (graph theory)Crystal (programming language)Second-harmonic imaging microscopyNonlinear opticsLaserMaximum power principleDopingOptoelectronicsEnergy conversion efficiencyDiodePower (physics)Physics

Abstract

fetched live from OpenAlex

Abstract In this paper, a systematic study of the relationship between nonlinear crystal length and intracavity second-harmonic generation (SHG) using MgO-doped periodically-poled lithium niobate (MgO:PPLN) is presented. The experimental results demonstrate a relationship between the maximum SHG power generated and the full-width at half maximum (FWHM) of the crystal’s temperature tuning curve to the length of the nonlinear optical crystal. It was shown that maximum SHG power increases rapidly with the increase of MgO:PPLN length, reaching a saturation length (~2 mm), which is much shorter than that predicted by the single-pass SHG theory. This saturation length of the MgO:PPLN crystal is almost independent on 808 nm pump power for typical powers used in continuous wave intracavity SHG lasers. In addition to this saturation effect, a broadening effect was also observed, the FWHM of the temperature tuning curve was shown to have a larger FWHM than that predicted by the single-pass SHG theory for MgO:PPLN shorter than the saturation length. This work has the benefit of allowing engineers to optimize nonlinear crystal length when developing intracavity SHG based diode-pumped solid state (DPSS) lasers.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.067
GPT teacher head0.420
Teacher spread0.353 · 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 designObservational
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
Published2021
Admission routes2
Has abstractyes

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