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Record W4408602337 · doi:10.1117/12.3043859

Modelling photo-detection efficiency in SiPMs and measuring the quantum yield of silicon: a flexible model from the UV to the IR

2025· article· en· W4408602337 on OpenAlexaff
A. De St. Croix, F. Retière, H Lewis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsTRIUMF
Fundersnot available
KeywordsSiliconOptoelectronicsYield (engineering)Quantum yieldQuantum efficiencyQuantumMaterials scienceSilicon photomultiplierComputer scienceOpticsPhysicsTelecommunicationsDetectorFluorescenceQuantum mechanics

Abstract

fetched live from OpenAlex

We will present an experimentally verified model for characterizing the behavior of Single Photon Avalanche Diodes (SPADs) and silicon photomultipliers (SiPMs). This work has been performed in the context of understanding large-scale, next generation physics experiments utilizing a large number of SiPMs. Measurements have been taken under illumination from the UV to the NIR at various bias voltages, angles of incidence and temperatures. This permits a detailed description of optical transmission into the device, the internal structure, and the probabilities of charge carriers producing an avalanche in different regions of the device. We will also present novel measurements of the so-called ‘quantum yield’ in silicon, the number of electron-hole pairs produced in an Si crystal per photon. This has been measured for high energy photons below 350nm via a novel SiPM based technique. This quantum yield information is incorporated into the framework for relevant photon energies. This model can aid in assessing detector performance across a range of optical inputs, characterize nuisance parameters such as optical cross-talk and inform the design of new devices with higher efficiency.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.248

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 head0.030
GPT teacher head0.229
Teacher spread0.199 · 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 designSimulation or modeling
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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