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Record W4388760736 · doi:10.1364/ome.503513

Effect of the nanopillar diameter on diamond silicon vacancy center spin lifetime

2023· article· en· W4388760736 on OpenAlexfundno aff
Thomas A. Lutz, Tamiko Masuda, John P. Hadden, Ilja Fescenko, Víctor M. Acosta, Wolfgang Tittel, Paul E. Barclay

Bibliographic record

VenueOptical Materials Express · 2023
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsnot available
FundersAlberta InnovatesEuropean CommissionCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsNanopillarMaterials scienceDiamondVacancy defectLaser linewidthSiliconPopulationSpinsCondensed matter physicsOptoelectronicsMolecular physicsNanotechnologyNanostructureOpticsPhysicsLaser

Abstract

fetched live from OpenAlex

Color centers in diamond micro and nano-structures play an important role in a wide range of quantum technologies. However, obtaining high-quality color centers in small structures is challenging, as properties such as spin population lifetimes can be affected by the transition from a bulk to nanostructured crystal host. In this manuscript, we measure how population lifetimes of silicon vacancy center orbital states change when they are created in nanopillars whose diameters vary from 1 μ m to 120 nm. We also discuss the influence of annealing methods on the silicon vacancy inhomogeneous linewidth. After selecting a sample with low inhomogeneous broadening and patterning it with nanopillars, we expected that restricted vibrational modes in the smallest structures could extend spin population lifetimes. However, we found that this effect was masked by other effects that reduced population lifetimes, suggesting that imperfections in the crystal lattice or surface damage caused by etching can influence SiV spins.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.274
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

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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