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Record W2139245741 · doi:10.1109/ccece.2005.1557440

1/f noise in an amorphous selenium photo-detect

2006· article· en· W2139245741 on OpenAlexaff
Shaikh Hasibul Majid, Robert E. Johanson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNoise (video)Materials scienceFlicker noiseNoise powerOptoelectronicsResistive touchscreenDark currentImage noiseOpticsAmorphous solidShot noiseDetectorPhotodetectorPhysicsChemistryNoise figureElectrical engineeringPower (physics)Image (mathematics)CMOSComputer science

Abstract

fetched live from OpenAlex

In direct-conversion, flat-panel, X-ray image detectors, stabilized amorphous selenium (a-Se) is used as the photoconductive layer because of its reasonably high X-ray absorption, low dark current, and good electronic transport properties. The signal-to-noise ratio of these detectors depends in part on the conductance noise in the selenium layer with the lower noise level providing better image quality. Modeling the performance of these detectors is hampered because noise studies of a-Se have not been reported in the literature perhaps because a-Se is a highly resistive material making such measurements difficult. We report here the first results for the low-frequency conductance noise in layers of a-Se under conditions similar to those found in the detectors. The noise power spectrum fits a 1/f power law with a in the range 0.77 to 1.5. Interpretation of the noise spectra is complicated due to the sample's highly nonlinear I-V relation. However, the noise spectrum depends critically on the type of metal evaporated on the a-Se surface for electrical contacts which indicates that the noise is controlled by the metal-semiconductor interface. Three types of metal electrodes have been measured-platinum, gold, and aluminum

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.486

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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".

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Citations0
Published2006
Admission routes1
Has abstractyes

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