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Record W4327696244 · doi:10.1117/12.2647263

Optomechanical cavities in silicon-on-insulator

2023· article· en· W4327696244 on OpenAlexaff
Jianhao Zhang, Paula Nuño-Ruano, Xavier Le Roux, Éric Cassan, Delphine Marris‐Morini, Laurent Vivien, Daniel Lanzillotti-Kimura, Carlos Alonso‐Ramos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsNational Research Council Canada
FundersAgence Nationale de la Recherche
KeywordsOptomechanicsSilicon on insulatorPhotonicsOptoelectronicsSilicon photonicsSiliconPhotonPhysicsCladding (metalworking)PhononQuantum opticsMaterials scienceOpticsResonator

Abstract

fetched live from OpenAlex

Integrated optomechanical cavities allow precise control of optical and mechanical modes and enable strong photonphonon interactions in micron-scale volume, key for the implementation of microwave-photonic oscillators and quantum transducers. Silicon photonics provides low production cost and compatibility with the state-of-art optoelectronic circuitry. Thus, it is particularly interesting for the implementation of on-chip optomechanics. However, silicon has higher stiffness and acoustic velocity than the silica cladding, hampering phonon confinement in silicon-on-insulator (SOI) waveguides. Here, we present our most recent results on SOI optomechanical systems coupling mechanical and guided optical modes. The cavities use silicon pillars with subwavelength period. Strong radiation pressure is exploited to drive the optomechanical coupling. Based on this concept, we experimentally demonstrate the optomechanical coupling between photons and high-quality factor phonons in non-suspended cavities, with a great potential for applications in quantum and classical photonics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 science0.0010.000
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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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Same topicMechanical and Optical ResonatorsFrench-language works237,207