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Record W2108787423 · doi:10.1109/smelec.2002.1217766

a-Si:H back-plane electronics for medical imaging and OLED displays

2004· article· en· W2108787423 on OpenAlexafffund
Arokia Nathan, K. Sakariya, K. S. Karim, Peyman Servati, Anil Kumar, Denis Striakhilev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsActive matrixOLEDAMOLEDThin-film transistorPixelMaterials scienceOptoelectronicsFlat panel displayBackplaneActive layerElectronic circuitThreshold voltageTransistorAmorphous siliconIntegrated circuitComputer scienceSiliconVoltageElectrical engineeringLayer (electronics)EngineeringCrystalline siliconArtificial intelligenceNanotechnology

Abstract

fetched live from OpenAlex

This paper reviews design considerations along with measurement results pertinent to amorphous silicon (a-Si:H) circuits for active matrix flat panel imagers (AMFPI) and active matrix organic light emitting diode (AMOLED) displays. We describe both pixel architectures and thin film transistor (TFT) circuit topologies that are amenable for vertically integrated, high aperture ratio or high fill factor pixels. Here, the OLED or detector layers are integrated directly above the TFT circuit layer, to provide an active pixel area that is at least 80% of the total pixel area with an aperture ratio or fill factor that remains virtually independent of scaling. Integration of on-panel gate drivers is also presented where we discuss an a-Si:H based gate de-multiplexer architecture that is threshold voltage shift invariant.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.551
Threshold uncertainty score0.444

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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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".

Quick stats

Citations1
Published2004
Admission routes2
Has abstractyes

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