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Record W4399166326 · doi:10.1021/acs.nanolett.3c04953

High <i>Q</i>-Factor Diamond Optomechanical Resonators with Silicon Vacancy Centers at Millikelvin Temperatures

2024· article· en· W4399166326 on OpenAlexfundno aff
Graham Joe, Cleaven Chia, Benjamin Pingault, Michael Haas, Michelle Chalupnik, Eliza Cornell, Kazuhiro Kuruma, Bartholomeus Machielse, Neil Sinclair, Srujan Meesala, Marko Lončar

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsArmy Research OfficeAir Force Office of Scientific ResearchOffice of Naval ResearchAgency for Science, Technology and ResearchJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsDiamondResonatorSiliconMaterials scienceVacancy defectOptoelectronicsQ factorCondensed matter physicsPhysicsComposite material

Abstract

fetched live from OpenAlex

Phonons are envisioned as coherent intermediaries between different types of quantum systems. Engineered nanoscale devices, such as optomechanical crystals (OMCs), provide a platform to utilize phonons as quantum information carriers. Here we demonstrate OMCs in diamond designed for strong for interactions between phonons and a silicon vacancy (SiV) spin. Using optical measurements at millikelvin temperatures, we measure a line width of 13 kHz ( Q -factor of ∼4.4 × 10 5 ) for a 6 GHz acoustic mode, a record for diamond in the GHz frequency range and within an order of magnitude of state-of-the-art line widths for OMCs in silicon. We investigate SiV optical and spin properties in these devices and outline a path toward a coherent spin–phonon interface.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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

Citations17
Published2024
Admission routes1
Has abstractyes

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