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Record W4416666551 · doi:10.60928/486s-zwmd

PlatonSPAD: A novel SPAD sensor for large-scale high-resolution particle detectors

2025· article· en· W4416666551 on OpenAlexaff
Kodai Kaneyasu, Till Dieminger, M. Franks, D. Sgalaberna, Claudio Bruschini, Edoardo Charbon

Bibliographic record

VenueArXiv.org · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsTracking (education)DetectorCMOSConvertersNoise (video)Photon countingSingle-photon avalanche diodeProbabilistic logicParticle detectorIntegrated circuit

Abstract

fetched live from OpenAlex

High-resolution 3D tracking with sub-nanosecond timing is required for the detection of elementary particles, such as neutrinos. Conventional detectors, which utilize analog silicon photomultipliers, face challenges in balancing spatial resolution and scalability. To address this issue, a CMOS single-photon avalanche diode (SPAD)-based high-resolution particle detector is being developed. This work presents a study on SPAD layout optimization and a 4x4 SPAD macropixel module, fabricated in 110 nm CIS technology. Measurement results confirm that high-fill-factor designs improve photon detection efficiency without significant noise degradation. Furthermore, event-driven photon mapping and time stamping, enabled by time-to-digital converters and dedicated pixel circuits integrated into the 4x4 SPAD macropixel, were successfully demonstrated. This work is an essential step towards a sensor that detects probabilistic particle interactions and it lays the groundwork for the development of future large-scale SPAD-based particle detectors.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.581

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 integrity0.0000.000
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 head0.020
GPT teacher head0.267
Teacher spread0.246 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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