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Record W2047220965 · doi:10.1142/s0129156408006004

NANOCRYSTALLINE SILICON THIN FILM TRANSISTORS FOR HIGH PERFORMANCE LARGE AREA ELECTRONICS

2008· article· en· W2047220965 on OpenAlexaff
Mohammad R. Esmaeili-Rad, Hyun Jung Lee, Andrei Sazonov, Arokia Nathan

Bibliographic record

VenueInternational Journal of High Speed Electronics and Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThin-film transistorMaterials scienceTransistorOptoelectronicsElectronicsThreshold voltageTransconductanceElectronic circuitAmorphous siliconNanocrystalline siliconNanocrystalline materialLeakage (economics)SiliconElectrical engineeringBackplaneElectronic engineeringEngineering physicsVoltageNanotechnologyEngineeringCrystalline silicon

Abstract

fetched live from OpenAlex

Nanocrystalline silicon ( nc - Si ) thin film transistors (TFTs) have potential for high-performance applications in large area electronics, such as next generation of flat panel displays and medical x-ray imagers, for pixel drivers, readout circuits, as well as complementary channel logic circuits for system-on-panel integration. This potential stems from reduced threshold voltage shift and higher transconductance, compared to amorphous silicon counterpart. In this paper, we discuss various TFT structures, their associated design and performance considerations, including leakage current and threshold voltage stability mechanisms.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.911

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations0
Published2008
Admission routes1
Has abstractyes

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