MétaCan
Menu
Back to cohort
Record W4410383714 · doi:10.1088/1361-6463/add946

Waveguide integrated superconducting nanowire single-photon detectors for integrated photonics

2025· article· en· W4410383714 on OpenAlexafffund
Vidur Raj, Adan Azem, Devendra K. Namburi, Jeff F. Young, Robert H. Hadfield

Bibliographic record

VenueJournal of Physics D Applied Physics · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of British Columbia
FundersScience and Technology Facilities CouncilEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsNanowirePhotonicsWaveguideDetectorPhotonOptoelectronicsSuperconductivityOpticsPhotonic integrated circuitPhysicsMaterials scienceCondensed matter physics

Abstract

fetched live from OpenAlex

Abstract Integrated photonics is expected to play a key role in the scalability of quantum systems for applications such as quantum computing, quantum communications, quantum internet, and quantum metrology. One of the primary components of quantum integrated photonics is a single photon detector, which reads out the quantum information encoded in photons. Amongst available single-photon detection schemes, superconducting nanowire single photon detectors (SNSPDs) remain the most promising technology for effective on-chip coupling, because they can be seamlessly integrated with a wide range of waveguide materials and substrates and have shown unparalleled performance from visible to the mid-infrared regime. Here, we review different aspects of SNSPDs and schemes for their on-chip integration for different integrated photonics applications. Although mostly concentrated on quantum applications, we also cover some of the important wider photonics applications including imaging, AI and machine learning, and single-photon spectroscopy, and conclude the review with a future outlook discussing emerging research areas enabled by photonic integrated circuits based on SNSPDs.

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 score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.234
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.

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

Citations6
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Physics D Applied PhysicsSame topicPhotonic and Optical DevicesFrench-language works237,207