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Record W2757337418 · doi:10.1109/trpms.2017.2757444

Low Power and Small Area, 6.9 ps RMS Time-to-Digital Converter for 3-D Digital SiPM

2017· article· en· W2757337418 on OpenAlexafffund
N. Roy, F. Nolet, Frédérik Dubois, Marc-Olivier Mercier, Réjean Fontaine, J.‐F. Pratte

Bibliographic record

VenueIEEE Transactions on Radiation and Plasma Medical Sciences · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsSilicon photomultiplierCMOSTime-to-digital converterVernier scaleElectrical engineeringPhysicsElectronic engineeringDigital electronicsElectronic circuitComputer scienceDetectorEngineeringScintillatorOpticsClock signal

Abstract

fetched live from OpenAlex

Time-of-flight measurements are becoming essential to the advancement of several fields, such as preclinical positron emission tomography and high energy physics. Recent developments in single photon avalanche diode (SPAD)-based detectors have spawned a great interest in digital silicon photomultipliers (dSiPMs). To overcome the tradeoff between the photosensitive area and the processing capabilities in current 2-D dSiPM, we propose a novel 3-D digital SiPM, where the SPAD, designed for maximal photosensitive area, will be stacked in 3-D over the electronic circuits, designed in a CMOS node technology. All readout circuits will be implemented directly under the SPAD real estate, including quenching circuit, time-to-digital converter (TDC) and digital readout electronics. This paper focusses on the TDC element of this system, designed in TSMC CMOS 65 nm. This ring oscillator-based Vernier TDC requires only 25 × 50 μm2and 160 μW, and achieves 6.9 ps rms timing accuracy.

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)0.001
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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

Citations42
Published2017
Admission routes2
Has abstractyes

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