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Record W3200837914 · doi:10.1088/1367-2630/ac291f

Generating T centres in photonic silicon-on-insulator material by ion implantation

2021· article· en· W3200837914 on OpenAlexaff
E. R. MacQuarrie, Camille Chartrand, Daniel Higginbottom, Kevin J. Morse, V. A. Karasyuk, S. Roorda, Stephanie Simmons

Bibliographic record

VenueNew Journal of Physics · 2021
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité de MontréalSimon Fraser UniversityPhoton Etc (Canada)
Fundersnot available
KeywordsPhysicsSiliconIon implantationPhotonPhotonicsFabricationOptoelectronicsWaferIonAnnealing (glass)Silicon on insulatorSemiconductorAtomic physicsOptics

Abstract

fetched live from OpenAlex

Global quantum networks will benefit from the reliable fabrication and control of high-performance solid-state telecom photon-spin interfaces. T radiation damage centres in silicon provide a promising photon-spin interface due to their narrow O -band optical transition near 1326 nm and long-lived electron and nuclear spin lifetimes. To date, these defect centres have only been studied as ensembles in bulk silicon. Here, we fabricate high concentration T centre ensembles in the 220 nm device layer of silicon-on-insulator wafers by ion implantation and subsequent annealing. We then develop a method that uses spin-dependent optical transitions to benchmark the characteristic optical spectral diffusion within these T centre ensembles. Using this new technique, we show that with minimal optimization to the fabrication process high densities of implanted T centres localized ≲100 nm from an interface display ∼1 GHz characteristic levels of total spectral diffusion.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.305

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations39
Published2021
Admission routes1
Has abstractyes

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