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Record W4281764113 · doi:10.1088/1361-6463/ac6e9c

Fluctuations in the collected charge in integrating photoconductive detectors under small and large signals: the variance problem

2022· article· en· W4281764113 on OpenAlexafffund
Kieran Oliver Ramaswami, Richard J. Curry, Ian Hinder, Robert E. Johanson, Safa Kasap

Bibliographic record

VenueJournal of Physics D Applied Physics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsUniversity of Saskatchewan
FundersEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaUniversity of ManchesterUniversity of Saskatchewan
KeywordsAlgorithmDetectorCharge (physics)Computer scienceDatabaseArtificial intelligencePhysicsTelecommunicationsParticle physics

Abstract

fetched live from OpenAlex

Abstract Charge collection efficiency (CE) η 0 under small signal conditions, corresponding to a uniform field in the detector medium, has been widely used in evaluating the performance of photoconductive detectors. The present paper answers the question, ‘What is the variance of the collected charge in an integrating detector as a function of photoinjection level and what are the errors if we continue to use the small signal equations?’ The variance <?CDATA $\sigma _0^2$?> σ 0 2 in η 0 under small signals has been theoretically derived in the literature and has been a key factor in the detective quantum efficiency modeling of integrating detectors based on various semiconductors. <?CDATA $\sigma _0^2$?> σ 0 2 is a noise source and can degrade the detector performance under incomplete charge collection. The statistical variance <?CDATA $\sigma _0^2$?> σ 0 2 in the CE η 0, under small signals and the variance <?CDATA $\sigma _r^2$?> σ r 2 in the CE ηr under an arbitrary injection level r (injected charge divided by charge on the electrodes) have been studied using the Monte Carlo simulation model developed in this work to evaluate the difference between <?CDATA $\sigma _r^2$?> σ r 2 and <?CDATA $\sigma _0^2$?> σ 0 2 from small to large signals. Initial injection of electron and hole pairs and their subsequent transport and trapping in the presence of an electric field, which is calculated from the Poisson equation, is used to calculate the photocurrent. Each injected carrier is tracked as it moves in the semiconductor until it is either trapped or reaches the collection electrode. Trapped carriers do not contribute to the photocurrent but continue to contribute to the field through the Poisson equation. The instantaneous photocurrent i ph(t) is calculated from the drift of the free carriers through the Shockley–Ramo theorem. i ph(t) is integrated over the duration of the photocurrent to calculate the total collected charge and hence the CE ηr . The variance <?CDATA $\sigma _r^2$?> σ r 2 in ηr is found from multiple simulations of ηr . The ηr and <?CDATA $\sigma _r^2$?> σ r 2 have been generated over varying charge injection ratios r, the electron and hole ranges μτ, mean photoinjection depths δ and drift mobility ratios b. At full injection, the deviation <?CDATA $\Delta \sigma _r^2$?> Δ σ r 2 of the CE variance <?CDATA $\sigma _r^2$?> σ r 2 from the uniform field case <?CDATA $\sigma _0^2$?> σ 0 2 ( <?CDATA ${\text{i}}.{\te

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)0.005
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.229
Teacher spread0.212 · 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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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