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Record W1998687154 · doi:10.1117/12.873484

Ultra wideband integrated polarization splitter/combiner using electro-optic effect in GaAs

2011· article· en· W1998687154 on OpenAlexaff
Mohamed A. Swillam, Mohamed H. Bakr, Xun Li

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsBandwidth (computing)SplitterBeam splitterFabricationComputer scienceOpticsMaterials sciencePolarization (electrochemistry)Refractive indexPockels effectWidebandOptoelectronicsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

We propose a novel design of integrated polarization splitter/combiner with ultra wide bandwidth. The proposed design utilizes the electro-optic (Pockels) effect in GaAs for splitting the polarizations. It also exploits the self imaging phenomenon in MMI couplers with a parabolic index distribution in the vertical direction to significantly increase the bandwidth. A stair case index approximation of this index profile is utilized to facilitate the fabrication process. The fabrication of this profile is feasible through the current technology using multiple etching. Our proposed design maintains a variation of less than 0.5 dB in the power coupling over a bandwidth of 400 nm. We also propose a novel approach for design optimization of the proposed structure. This approach is capable of extracting the propagation constants and their gradient with respect to all the design parameters. This allows for using gradient-based optimization The computational time of this optimization procedure is only a fraction of that for other recently proposed approaches.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0010.000
Open science0.0010.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE→Same topicPhotonic and Optical Devices→French-language works237,207→