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Record W4283653391 · doi:10.3390/electronics11132015

A Multi-Time-Gated SPAD Array with Integrated Coarse TDCs

2022· article· en· W4283653391 on OpenAlexafffund
Ryan P. Scott, Wei Jiang, Xuanyu Qian, M. Jamal Deen

Bibliographic record

VenueElectronics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsSingle-photon avalanche diodeCMOSOptoelectronicsGate arrayDiodePhotodetectorAvalanche photodiodePhysicsDetectorComputer scienceOpticsComputer hardwareField-programmable gate array

Abstract

fetched live from OpenAlex

Time-gating of single-photon avalanche diodes (SPADs) was commonly used as a method to reduce dark noise in biomedical imaging applications where photon events are correlated with a reference clock. Time-gating was also used to obtain timing information of photon events by shifting the gate windows applied to a SPAD array. However, in this approach, fine timing resolution comes at the cost of a lengthened measurement time due to the large number of counts required for each shift. As a solution, we present a multi-time-gated SPAD array that simultaneously applies shifted gate windows to an array of SPADs, which has the potential to reduce the measurement time compared to a single time gate window. Compared to similar works, this design has fully integrated the multi-gate generation using shared circuitry which also functions as a coarse time-to-digital converter. The proposed array, fabricated in the TSMC 65 nm standard CMOS process, achieved a median dark count rate (DCR) of 37 kHz, 4.37 ns gate widths, 550 ps timing resolution, and a peak photon detection probability (PDP) of 42.9% at 420 nm, all at a 0.8 V excess bias.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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 designNot applicable
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

Citations8
Published2022
Admission routes2
Has abstractyes

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