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Record W1578341820 · doi:10.1002/lpor.201200023

Polarization management for silicon photonic integrated circuits

2012· article· en· W1578341820 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLaser & Photonics Review · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsInstitute for Microstructural Sciences
Fundersnot available
KeywordsSilicon on insulatorNanophotonicsPolarization (electrochemistry)Polarization mode dispersionPhotonicsSilicon photonicsMaterials scienceOptoelectronicsPhotonic integrated circuitPolarization rotatorSiliconOpticsPhysicsBirefringenceOptical fiberChemistry

Abstract

fetched live from OpenAlex

Abstract Polarization management is very important for photonic integrated circuits (PICs) and their applications. Due to geometrical anisotropy and fabrication inaccuracies, the characteristics of the guided transverse‐electrical (TE) and transverse‐magnetic (TM) modes are generally different. Polarization‐dependent dispersion and polarization‐dependent loss are such manifestations in PICs. These issues become more severe in high index contrast structures such as nanophotonic waveguides made of silicon‐on‐insulator (SOI), which has been regarded as a good platform for optical interconnects because of the compatibility with CMOS processing. Recently, polarization division multiplexing (PDM) with coherent detection using silicon photonics has also attracted much attention. This trend further highlights the importance of polarization management in silicon PICs. The authors review their work on polarization management for silicon PICs using the polarization independence and polarization diversity methods. Polarization issues and solutions in PICs made of SOI nanowires and ridge waveguides are discussed.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.964

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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