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Record W4312339572 · doi:10.1109/jlt.2022.3217976

Integrated Phased Array for Scalable Vortex Beam Multiplexing

2022· article· en· W4312339572 on OpenAlexafffund
Yuxuan Chen, Simon Levasseur, Leslie A. Rusch, Wei Shi

Bibliographic record

VenueJournal of Lightwave Technology · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMultiplexerMultiplexingOptical vortexScalabilityBeamformingPhysicsOptical communicationOpticsTopology (electrical circuits)Electronic engineeringComputer scienceBeam (structure)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Orbital angular momentum (OAM) modes have low model interactions during fiber propagation at data center distances, and thus are suitable for ultra-high capacity systems at low digital signal processing. Generating OAM modes using free-space setups is useful for proof-of-concept experiments, but is not a scalable solution. We use an optical phased array (OPA) with two-dimensional antennas for on-chip circularly polarized OAM beam generation. Our previous work demonstrated an OAM multiplexer for lower-order modes. In this work, we demonstrate an OAM multiplexer that supports a record of 46 (23 per polarization) simultaneous spatial modes up to OAM order 11. We also improve the crosstalk performance of our multiplexer. We incorporate an intensity tuning capability that substantially improves the OAM quality by enabling a uniform power distribution across the antennas. The worst-case crosstalk for the supported OAM5 to OAM11 are found experimentally to be better than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-12$</tex-math></inline-formula> dB, with OAM10 achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-17.2$</tex-math></inline-formula> dB.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.630

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.243
Teacher spread0.233 · 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
Published2022
Admission routes2
Has abstractyes

Explore more

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