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Record W3200044124 · doi:10.1109/jlt.2021.3111119

Differential Quench and Reset Circuit for Single-Photon Avalanche Diodes

2021· article· en· W3200044124 on OpenAlexafffund
Wei Jiang, Ryan P. Scott, M. Jamal Deen

Bibliographic record

VenueJournal of Lightwave Technology · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsReset (finance)DiodePhysicsElectronic circuitNoise (video)OptoelectronicsElectrical engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

A differential quench and reset (QR) configuration for single-photon avalanche diodes (SPADs) is proposed and investigated in this work. Compared to the traditional single QR configuration in which only one QR circuit is connected to either the cathode or anode of the SPAD, the differential QR configuration consists of two QR circuits to quench and reset through both the cathode and anode terminals. Using a SPAD fabricated in a 65 nm CMOS process, the measurement results of the traditional single passive quench and reset (SPQR) circuit and the proposed differential passive quench and reset (DPQR) circuit shows that the SPAD with the DPQR circuit presented a reduced reset time, an improved timing performance, an increased count rate and a decreased afterpulsing. In addition, the differential output pair from the DPQR circuit has the potential to increase the signal-to-noise ratio of the detection system due to its ability to reject common-mode noise and interference.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2021
Admission routes2
Has abstractyes

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