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Record W2056436111 · doi:10.1364/ao.45.005391

Picosecond-pulse wavelength conversion based on cascaded second-harmonic generation-difference frequency generation in a periodically poled lithium niobate waveguide

2006· article· en· W2056436111 on OpenAlexaff
Yong Wang, Jorge Fonseca-Campos, Chang‐Qing Xu, Shiquan Yang, E. A. Ponomarev, Xiaoyi Bao

Bibliographic record

VenueApplied Optics · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Fiber Laser Technologies
Canadian institutionsUniversity of OttawaMcMaster University
Fundersnot available
KeywordsOpticsLithium niobatePicosecondMaterials scienceEnergy conversion efficiencyWaveguideWavelengthLasing thresholdSecond-harmonic generationQuasi-phase-matchingOptoelectronicsLaserPhysics

Abstract

fetched live from OpenAlex

The wavelength conversion of picosecond optical pulses based on the cascaded second-harmonic generation-difference-frequency generation process in a MgO-doped periodically poled lithium niobate waveguide is studied both experimentally and theoretically. In the experiments, the picosecond pulses are generated from a 40 GHz mode-locked fiber laser and two tunable filters, with which the lasing wavelength can be tuned from 1530 to 1570 nm, and the pulse width can be tuned from 2 to 7 ps. New-frequency pulses, i.e., converted pulses, are generated when the picosecond pulse train and a cw wave interact in the waveguide. The conversion characteristics are systematically investigated when the pulsed and cw waves are alternatively taken as the pump at the quasi-phase-matching wavelength of the device. In particular, the conversion dependences on input pulse width, average power, and pump wavelength are examined quantitatively. Based on the temporal and spectral characteristics of wavelength conversion, a comprehensive analysis on conversion efficiency is presented. The simulation results are in good agreement with the measured data.

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 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 score0.292
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.222
Teacher spread0.208 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

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