MétaCan
Menu
Back to cohort
Record W2119043660 · doi:10.1109/jsen.2011.2123090

High-Speed, Single-Photon Avalanche-Photodiode Imager for Biomedical Applications

2011· article· en· W2119043660 on OpenAlexaff
Darek Palubiak, Munir M. El‐Desouki, Ognian Marinov, M. Jamal Deen, Qiyin Fang

Bibliographic record

VenueIEEE Sensors Journal · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSingle-photon avalanche diodeAvalanche photodiodeCMOSPixelPhoton countingImage sensorDetectorPhotodiodeCMOS sensorOptoelectronicsAvalanche diodePhotodetectorOpticsComputer scienceMaterials sciencePhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

The design of a low-light level pixel in CMOS technology for biomedical applications is described. This pixel is also suitable for very high-speed applications, such as fluorescence lifetime imaging (FLIM) used for drug discovery and/or minimally-invasive optical biopsy. In order to achieve high-speed imaging using single-photon detection, a detector with a very low dead-time is needed. The single-photon avalanche-photodiode (SPAD) discussed in this work uses a mainstream deep-submicron CMOS technology in order to achieve ultrahigh-speed operation and high pixel fill-factor, with in-pixel active quench and reset circuits. The paper also presents an innovative approach for reducing the deadtime of the detector and an attractive technique for simultaneous high-speed image acquisition by all the pixels of an array in parallel.

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: none
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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.261
Teacher spread0.230 · 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

Citations107
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Sensors JournalSame topicAdvanced Optical Sensing TechnologiesFrench-language works237,207