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Record W4220895044 · doi:10.1117/12.2610477

Iterative and efficient Bloch mode calculations for integrated photonics applications

2022· article· en· W4220895044 on OpenAlexaff
Mohsen Kamandar Dezfouli, Daniele Melati, Yuri Grinberg, Muhammad Al-Digeil, Ross Cheriton, Pavel Cheben, Jens H. Schmid, Siegfried Janz, Dan‐Xia Xu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPhotonicsNanophotonicsApodizationComputer sciencePhotonic crystalFrequency domainPeriodic boundary conditionsMatching (statistics)Boundary (topology)Integrated opticsElectronic engineeringOpticsBoundary value problemPhysicsEngineeringMathematics

Abstract

fetched live from OpenAlex

Design of novel integrated photonic components often benefits from periodic geometries (either fully periodic or apodized) along the direction of light propagation, offering a wide range of capabilities including mode matching and optical rerouting. Here, we show how existing iterative methods that were originally developed for resonant nanophotonic systems in the frequency domain can be reliably used for calculation of optical Bloch modes in periodic systems in the complex wavevector domain. This method can be used for arbitrary shaped geometries and even when open boundary conditions are applied, therefore heavily impacting the fast-paced design of integrated photonic devices.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.243
Teacher spread0.234 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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