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Record W2055304672 · doi:10.1049/ip-cds:20030554

Thin film transistor integration on glass and plastic substrates in amorphous silicon technology

2003· article· en· W2055304672 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

VenueIEE Proceedings - Circuits Devices and Systems · 2003
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsActive matrixThin-film transistorAMOLEDAmorphous siliconMaterials scienceTransistorOptoelectronicsOLEDActive layerPixelAperture (computer memory)SiliconDetectorOxide thin-film transistorFlat panel displayLayer (electronics)OpticsCrystalline siliconNanotechnologyElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The authors review amorphous silicon (a-Si:H) thin film transistor (TFT) integration and design considerations, including stability, and present examples of integration for two application areas: active matrix organic light emitting diode (AMOLED) displays and active matrix flat panel imagers (AMFPIs) for medical imaging. Pixel architectures and TFT circuit topologies are described 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. The design is based on physically-based compact TFT models, which accurately predict both static and dynamic behaviour.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
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
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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