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Record W2005092773 · doi:10.1364/ao.47.006625

Modified Fabry-Perot interferometric method for waveguide loss measurement

2008· article· en· W2005092773 on OpenAlexaff
Sareh Taebi, Mohammadreza Khorasaninejad, Simarjeet S. Saini

Bibliographic record

VenueApplied Optics · 2008
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOpticsFabry–Pérot interferometerInterferometryMaterials scienceLaserWaveguideBroadbandWavelengthOptical powerSemiconductor laser theoryPhysics

Abstract

fetched live from OpenAlex

An extension to the Fabry-Perot interferometric method is demonstrated to calculate the optical loss and the reflectivity for optical waveguides simultaneously. The method uses an excitation of the waveguide with a broadband amplified spontaneous emission source (a superluminescent diode in our case) and curve fitting to account for the change of input power, thereby simplifying the measurement procedure. The use of a broadband source as opposed to tunable lasers allows for simultaneous measurements over multiple wavelengths and decreased sensitivity to reflections in the cavity. Further, waveguides of different lengths are measured to calculate the optical loss and the reflectivity simultaneously. It is shown that, if the value for reflectivity is assumed, there could be a large error in the measurement of loss especially for short waveguides. Optical loss for ridge waveguides is measured and compared by using a tunable laser as the input source. The method can be used for a generic case where it is suspected that the input power changes during the measurement.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.049
GPT teacher head0.256
Teacher spread0.207 · 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

Citations44
Published2008
Admission routes1
Has abstractyes

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