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Record W2127106059 · doi:10.1109/ted.2008.926744

Two-Transistor Active Pixel Sensor Readout Circuits in Amorphous Silicon Technology for High-Resolution Digital Imaging Applications

2008· article· en· W2127106059 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Electron Devices · 2008
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPixelDot pitchTransistorImage resolutionImage sensorMaterials scienceCMOS sensorCMOSNoise (video)OptoelectronicsElectronic engineeringComputer scienceOpticsElectrical engineeringPhysicsVoltageEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Active pixel sensor (APS) architectures using two transistors per pixel are reported in this paper for high-resolution low-noise digital imaging applications. The fewer number of on-pixel elements and reduced pixel complexity result in a smaller pixel pitch and increased pixel gain, which makes the two-transistor (2T) APS architectures promising for high-resolution, low-noise, and high-speed digital imaging including emerging medical imaging modalities, such as mammography tomosynthesis and cone beam computed tomography. Measured results from in-house fabricated test pixels using amorphous silicon (a-Si) thin-film transistors, as well as driving schemes for minimizing the threshold voltage metastability problem and increasing frame rate, are presented. The results indicate that a pixel input referred noise value of down to 220 electrons is achievable with a 50- -pixel-pitch a-Si 2T 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.

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 categoriesMeta-epidemiology (narrow)
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 score0.501
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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