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Record W2899068186 · doi:10.1109/jphot.2018.2878905

Compact, Angled Polarization Splitter: Characterization of Broadband Performance and Fabrication Tolerance

2018· article· en· W2899068186 on OpenAlexaff
Yannick D’Mello, Eslam El‐Fiky, James Skoric, Amar Kumar, Michael Hui, Yun Wang, Laurent Guenin, David Patel, David V. Plant

Bibliographic record

VenueIEEE photonics journal · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsFabricationBroadbandSplitterPolarization (electrochemistry)OptoelectronicsFiber optic splitterOpticsMaterials scienceOptical fiberPhysics

Abstract

fetched live from OpenAlex

We propose and experimentally demonstrate a broadband polarization beam splitter on the silicon-on-insulator platform using the concept of multimode interference. The angle between the input waveguide and the multimode interferometer (MMI) was optimized in order to extract the images corresponding to the fundamental transverse electric (TE) and transverse magnetic (TM) polarizations from a single, compact MMI. By changing the direction of propagation, the input angle enabled the addition of a parallelepiped to facilitate exclusively TM collection above the MMI, therefore eliminating the need to cascade successive MMIs. The MMI was tapered to improve coupling at the ports. The design allowed for a compact geometry with a length of 67.5 μm and width of 4 μm, while maintaining a feature size comparable to the waveguide width. The design was experimentally characterized over 72 parameter variations, including input angle, output TM and TE port positions, and bend radius of the output TE waveguide. The performance was consistent over the 100 nm wavelength range of 1500-1600 nm, with an average insertion loss of 2 dB. The device maintained an extinction ratio of at least 13.96 and 11.45 dB in the C-band for the TE and TM polarizations, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.384

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)0.0000.000

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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