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Record W4388040009 · doi:10.1021/acsphotonics.3c01142

High-Efficiency Single Photon Emission from a Silicon T-Center in a Nanobeam

2023· article· en· W4388040009 on OpenAlexafffund
Chang-Min Lee, Fariba Islam, Samuel Harper, Mustafa Atabey Buyukkaya, Daniel Higginbottom, Stephanie Simmons, Edo Waks

Bibliographic record

VenueACS Photonics · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsCoquitlam CollegeSimon Fraser University
FundersArmy Research LaboratoryAir Force Office of Scientific ResearchOffice of Multidisciplinary ActivitiesDivision of Electrical, Communications and Cyber SystemsDivision of PhysicsCanada Research ChairsUniversity of MarylandNatural Sciences and Engineering Research Council of CanadaU.S. Department of Defense
KeywordsPhotonPhysicsCoherence (philosophical gambling strategy)Single-photon sourceExcited stateSiliconOptoelectronicsQuantum opticsCenter (category theory)Quantum dotSpontaneous emissionOpticsAtomic physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Color centers in Si could serve as both efficient quantum emitters and quantum memories with long coherence times in an all-silicon platform. Of the various known color centers, the T-center holds particular promise because it possesses a spin ground state that has long coherence times, but this color center exhibits a long excited state lifetime that results in a low-photon emission rate, requiring methods to extract photon emission with high efficiency. We demonstrate high-efficiency single-photon emission from a single T-center using a nanobeam. The nanobeam efficiently radiates light in a mode that is well-matched to a lensed fiber, enabling us to couple over 70% of the photons in the nanobeam directly into a single-mode fiber. This efficiency enables us to directly demonstrate single-photon emission from the zero-phonon line, which represents the coherent emission from the T-center. Our results represent an important step toward silicon-integrated spin–photon interfaces for quantum computing and quantum networks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.232
Teacher spread0.217 · 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

Citations29
Published2023
Admission routes2
Has abstractyes

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