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Record W2911397390 · doi:10.1109/iedm.2018.8614645

CMOS-Integrated Single-Photon-Counting X-Ray Detector using an Amorphous-Selenium Photoconductor with <tex>$11\times 11-\boldsymbol{\mu}\mathbf{m}^{2}$</tex> Pixels

2018· article· en· W2911397390 on OpenAlexaff
Ahmet Çamlıca, Abdallah El-Falou, Reza Mohammadi, Peter M. Levine, K. S. Karim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhoton countingPixelPhysicsDetectorX-ray detectorAmorphous solidCMOSOptoelectronicsSemiconductorPhotoconductivityPhotonImage resolutionSemiconductor detectorOpticsMaterials scienceChemistryCrystallography

Abstract

fetched live from OpenAlex

We report, for the first time, results from a single-photon-counting X-ray detector monolithically integrated with an amorphous semiconductor. Our prototype detector combines amorphous selenium (a-Se), a well-known X-ray photoconductive material suitable for large-area applications, with a 0.18-μm-CMOS readout integrated circuit containing two 26×196 photon counting pixel arrays. The detector features 11×11-μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pixels to overcome a-Se count-rate limitations by unipolar charge sensing of the faster charge carriers (holes) via a unique pixel geometry that leverages the small pixel effect for the first time in an amorphous semiconductor. Measured results from a mono-energetic radioactive source are presented and demonstrate the untapped potential of using amorphous semiconductors for high-spatial-resolution photon-counting X-ray imaging applications.

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)0.001
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 categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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