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Record W2005105159 · doi:10.1364/oe.21.010160

Optimizing photonic crystal waveguides for on-chip spectroscopic applications

2013· article· en· W2005105159 on OpenAlexfundno aff
Andreas C. Liapis, Zhimin Shi, Robert W. Boyd

Bibliographic record

VenueOptics Express · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsnot available
FundersJoint Program Executive Office for Chemical, Biological, Radiological and Nuclear DefenseDefense Threat Reduction AgencyCanada Research Chairs
KeywordsSpectrometerOpticsFinessePhotonic crystalBandwidth (computing)PhotonicsOptoelectronicsFigure of meritMaterials scienceRefractive indexPhotonic integrated circuitChipPhysicsTelecommunicationsLaserComputer science

Abstract

fetched live from OpenAlex

We investigate the applicability of photonic crystal waveguides to high-resolution on-chip spectrometers. We argue that the figure of merit by which their performance should be gauged is not the group index bandwidth product, which photonic crystal waveguides are usually optimized for, but the working finesse, which relates to the maximum number of spectral lines resolvable by a slow-light spectrometer. Through numerical simulation, we show that a properly-optimized photonic crystal waveguide could form the basis of a spectrometer with a spectral resolution of 0.04 nm over a 12.5 nm bandwidth near 1550 nm and with a footprint six times smaller than a conventional spectrometer.

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 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 score0.791
Threshold uncertainty score0.951

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)0.0010.000

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 head0.015
GPT teacher head0.264
Teacher spread0.250 · 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 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

Citations15
Published2013
Admission routes1
Has abstractyes

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