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High performance silicon photonics filters for quantum applications

2024· article· en· W4399486552 on OpenAlexafffund
José Manuel Luque‐González, Alejandro Fernández‐Hinestrosa, Carlos Pérez‐Armenta, Alejandro OrtegaMoñux, Robert Halir, J. Gonzalo Wangüemert‐Pérez, Abdelfettah Hadij‐ElHouati, Pavel Cheben, Jens H. Schmid, Maziyar Milanizadeh, Shurui Wang, Kevan K. MacKay, Winnie N. Ye, Íñigo Molina‐Fernández

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsCarleton UniversityNational Research Council Canada
FundersMinisterio de Economía y CompetitividadNational Research CouncilNatural Sciences and Engineering Research Council of CanadaUniversidad de Málaga
KeywordsPhotonicsSilicon photonicsOptical filterOptoelectronicsComputer scienceSiliconElectronic engineeringMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Integrated optical filters are key building blocks in many applications including quantum optics, microwave photonics and telecommunications. Bragg grating structures stand out as a prominent solution for filtering in silicon chips because of their virtually infinite free-spectral range, high rejection ratios and narrow bandwidths. In this presentation we summarize two of our recent contributions in integrated Bragg filters including: i) a contra-directional coupler for quantum applications with a 40 dB rejection of the pump wavelength for both polarizations, and ii) a thermally tunable notch filter with controllable sidelobes. Both devices introduce new design approaches for optical systems demanding high performance spectral filtering.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.225
Teacher spread0.216 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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