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Record W3179522571 · doi:10.1364/ol.431983

Millimeter-long metamaterial surface-emitting antenna in the silicon photonics platform

2021· article· en· W3179522571 on OpenAlexaff
Pablo Ginel‐Moreno, Alejandro Sánchez‐Postigo, J. de‐Oliva‐Rubio, Abdelfettah Hadij‐ElHouati, Winnie N. Ye, J. Gonzalo Wangüemert‐Pérez, Íñigo Molina‐Fernández, Jens H. Schmid, Pavel Cheben, Alejandro Ortega‐Moñux

Bibliographic record

VenueOptics Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaCarleton University
FundersJunta de AndalucíaMinisterio de Ciencia, Innovación y UniversidadesUniversidad de Málaga
KeywordsOpticsMaterials scienceAntenna (radio)MetamaterialOptoelectronicsPhotonicsWaveguideSilicon photonicsBeam divergencePhysicsLaserBeam diameterTelecommunications

Abstract

fetched live from OpenAlex

Integrated optical antennas are key components for on-chip light detection and ranging technology (LIDAR). In order to achieve a highly collimated far field with reduced beam divergence, antenna lengths on the order of several millimeters are required. In the high-index contrast silicon photonics platform, achieving such long antennas typically demands weakly modulated gratings with lithographic minimum feature sizes below 10 nm. Here, we experimentally demonstrate a new, to the best of our knowledge, strategy to make long antennas in silicon waveguides using a metamaterial subwavelength grating (SWG) waveguide core loaded with a lateral periodic array of radiative elements. The mode field confinement is controlled by the SWG duty cycle, and the delocalized propagating mode overlaps with the periodic perturbations. With this arrangement, weak antenna radiation strength can be achieved while maintaining a minimum feature size as large as 80 nm. Using this strategy, we experimentally demonstrate a 2-millimeter-long, single-etched subwavelength-engineered optical antenna on a conventional 220 nm SOI platform, presenting a measured far-field beam divergence of 0.1° and a wavelength scanning sensitivity of 0.13°/nm.

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 score0.714
Threshold uncertainty score0.606

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 head0.014
GPT teacher head0.215
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 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

Citations34
Published2021
Admission routes1
Has abstractyes

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