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Record W2152617954 · doi:10.1109/jdt.2005.858913

Driving Schemes for a-Si and LTPS AMOLED Displays

2005· article· en· W2152617954 on OpenAlexaff
Arokia Nathan, G. Reza Chaji, Shahin Jafarabadi Ashtiani

Bibliographic record

VenueJournal of Display Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAMOLEDBackplaneOLEDActive matrixElectronic circuitMaterials scienceDiodeOptoelectronicsParasitic extractionElectronic engineeringComputer scienceElectrical engineeringEngineeringThin-film transistorNanotechnology

Abstract

fetched live from OpenAlex

Design of stable active matrix organic light-emitting diode (AMOLED) displays comes with significant challenges that stem from the electrical property of the backplane materials, line parasitics in the matrix, and the opto-electronic property of the organic light-emitting diode (OLED). This paper reviews voltage and current programming schemes for AMOLEDs. Following a systematic review of pixel circuits, design considerations are examined for both current and voltage schemes with focus on stability and programming speed for both amorphous silicon (a-Si) and low temperature polysilicon (LTPS) pixel circuits. In particular, spatial parameter variations and stability, which hinder reliable operation of AMOLED display backplanes, are discussed. Analysis shows that while driving schemes reported hitherto maybe suitable for small and medium size displays, new schemes are critically needed for large-area high-resolution AMOLED displays.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.213
Teacher spread0.209 · 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

Citations251
Published2005
Admission routes1
Has abstractyes

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