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A 3D Low-Power Photon-to-Digital Converter - Radiation Detection Applications

2025· article· W4417471442 on OpenAlexaffabout
T. Rossignol, C. Pépin, G. Lessard, F. Vachon, N. Roy, Romain Espagnet, G. Lemaire, A. L. Steinhebel, Paul Hausladen, L. Fabris, Roger Lecomte, R. Fontaine, Serge A. Charlebois, J.-F. Pratte

Bibliographic record

Venuenot available
Typearticle
Language
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNational Nuclear Security AdministrationU.S. Department of Energy
KeywordsTime-to-digital converterConvertersInstrumentation (computer programming)Full width at half maximumNuclear electronicsNeutronCMOS

Abstract

fetched live from OpenAlex

The completion of a first wafer lot of 3D Photon-to-Digital Converters at Sherbrooke enables a new instrumentation tool for different applications such as neutron imaging, particle physics, medical imaging, spectral lidar. While the custom 3D SPAD tier is being characterized, a complete system has been designed and tested using the CMOS readout of the PDC and an AMD Zynq Ultrascale+. This low-power flavor of the PDC readout has a flag output for timing measurement with an external Time-to-Digital Converter and coincidence algorithms, and a digital sum to get the number of detected photons. With these two pieces of information, one has the flexibility to perform various types of measurements such as pulse-height spectra, pulse-shape discrimination, and precise Time-of-Arrival estimation. A demonstration of the capabilities of the PDC acquisition system resulted in a timing precision of approximately 95 ps FWHM and a variation of 10 photons FWHM while using a PicoQuant laser.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.003
GPT teacher head0.233
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 teacher head, not a consensus.

Study designOther design
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

Citations0
Published2025
Admission routes2
Has abstractyes

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