MétaCan
Menu
Back to cohort
Record W2750956035 · doi:10.1117/12.2283926

Feasibility of current mediated amorphous silicon active pixel sensor readout circuits for large area diagnostic medical imaging

2017· article· en· W2750956035 on OpenAlexaff
Paul Johns

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFlicker noiseNoise (video)AmplifierDetectorPixelElectronic circuitMaterials scienceOptoelectronicsLinearityElectrical engineeringDark currentCMOSImage sensorElectronic engineeringComputer sciencePhysicsNoise figurePhotodetectorOpticsEngineeringArtificial 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 towards 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 [1] indicate excellent small signal linearity along with a high, and programmable, charge gain, which is promising for diagnostic medical imaging. This work examines low frequency a-Si TFT thermal and flicker noise, which contribute to the overall noise performance of the APS.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
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 head0.033
GPT teacher head0.302
Teacher spread0.269 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicCCD and CMOS Imaging SensorsFrench-language works237,207