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Record W2105888720 · doi:10.1109/55.954914

Readout circuit in active pixel sensors in amorphous silicon technology

2001· article· en· W2105888720 on OpenAlexaff
K. S. Karim, Arokia Nathan

Bibliographic record

VenueIEEE Electron Device Letters · 2001
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAmplifierNoise (video)PixelLinearityDetectorFixed-pattern noiseOptoelectronicsMaterials scienceElectrical engineeringCharge amplifierSiliconSIGNAL (programming language)Reading (process)Electronic engineeringCMOSPhysicsComputer scienceEngineeringOpticsOperational amplifierArtificial intelligence

Abstract

fetched live from OpenAlex

The most widely used architecture in large area amorphous silicon (a-Si) flat panel imagers is a passive pixel sensor (PPS), which consists of a detector and a readout switch. While the PPS has the advantage of being compact and amenable toward high-resolution imaging, reading small PPS output signals requires external circuitry such as column charge amplifiers that produce additional noise and reduce the minimum readable sensor input signal. This work presents a current mode amorphous silicon active pixel that performs on-pixel amplification of noise-vulnerable sensor input signals to minimize the effect of external readout noise sources associated with "off-chip" charge amplifiers. Preliminary results indicate excellent small signal linearity along with a high and programmable charge gain.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.212
Teacher spread0.204 · 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".

Quick stats

Citations49
Published2001
Admission routes1
Has abstractyes

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