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Silicon Optomechanical Membrane Waveguides Based on Subwavelength Engineering of Photons and Phonons

2023· article· en· W4385655996 on OpenAlexaff
Paula Nuño Ruano, Jianhao Zhang, Xavier Le Roux, Daniele Melati, David González‐Andrade, Éric Cassan, Delphine Marris‐Morini, Laurent Vivien, N. D. Lanzillotti‐Kimura, Carlos Alonso‐Ramos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsNational Research Council Canada
FundersErasmus+Agence Nationale de la Recherche
KeywordsOptomechanicsBrillouin scatteringBrillouin zoneSilicon on insulatorPhononPhotonOptoelectronicsSiliconSilicon photonicsOpticsCladding (metalworking)PhotonicsMaterials sciencePhysicsOptical fiberResonatorCondensed matter physics

Abstract

fetched live from OpenAlex

On-chip Brillouin optomechanics has great potential for applications in communications, sensing, and quantum technologies. However, achieving tight confinement of near-infrared photons and gigahertz phonons in integrated waveguides remains a crucial challenge to yield high on-chip Brillouin gain. This challenge is solved in silicon-on-insulator (SOI) waveguides by removing the silica under-cladding. Here we show that subwavelength engineering of the longitudinal and transversal dimensions of Si membranes facilitates independent control of the photonic and phononic modes, allowing for strong Brillouin scattering. Here, we present two strategies to realize suspended silicon waveguides simultaneously confining the optical and mechanical modes in the core, based on subwavelength engineering. These structures provide significant Brillouin gain and allow optomechanical coupling with high-frequency mechanical modes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.234
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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