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Monolithically Integrated Waveguide-Coupled Single-Photon Avalanche Photodetector in a Visible-Light Silicon Photonics Platform

2023· article· en· W4386427695 on OpenAlexaff
Alperen Gövdeli, John N. Straguzzi, Wesley D. Sacher, Joyce K. S. Poon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWaveguideOptoelectronicsPhotonicsPhotodetectorPhysicsSilicon photonicsQuantum efficiencySiliconPhotonDiodeSilicon nitrideLambdaOptics

Abstract

fetched live from OpenAlex

The integration of silicon nitride (SiN) waveguides into silicon (Si) photonic platforms extends Si photonic integrated circuits (PICs) to visible-light applications including quantum information [1], neurotechnology [2] and miniaturized display systems [3]. Si photodetectors (PDs) are essential components in such systems. Although conventional Si PDs rely on surface incidence-based free space detection, waveguide-coupled PDs are preferred in PICs due to their integrability with other PIC components. In [4], our group reported ultra-broadband SiN-on-Si waveguide PIN and PN PDs operating at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda=400$</tex>−640nm with an external quantum efficiency of >60% [4]. Such performance opens the possibility for single-photon avalanche diode (SPAD) operation. Although there are several examples of free-space SPADs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\lambda=490\text{nm})$</tex> [5] and waveguide-coupled SPADs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\lambda=1310\text{nm})$</tex> [6], here we report the preliminary characterization of, to best of our knowledge, the first monolithically integrated waveguide-coupled visible-light SPAD.

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 categoriesMeta-epidemiology (narrow)
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 score0.249
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.257
Teacher spread0.235 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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