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Record W2034989845 · doi:10.1116/1.1472427

Modeling of the static and dynamic behavior of hydrogenated amorphous silicon thin-film transistors

2002· article· en· W2034989845 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2002
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThin-film transistorAmorphous siliconMaterials scienceAmorphous solidTransistorSiliconOptoelectronicsVoltageComputer scienceNanotechnologyElectrical engineeringLayer (electronics)Crystalline siliconChemistryEngineeringCrystallography

Abstract

fetched live from OpenAlex

This article reports on physically based models for hydrogenated amorphous silicon (a-Si:H) inverted staggered thin-film transistors (TFT), which accurately predict both the static and dynamic characteristics of the TFT. The model is implemented in VerilogA hardware description language, which comes as a standard feature in most circuit simulation environments. The static model includes both forward and reverse regimes of operation. The model for leakage current takes into account the physical mechanisms responsible for the source of the reverse current, viz., the formation of the conducting channels at the back and front a-Si:H/a-SiNx:H interfaces and their relative dominance at different bias conditions. The dynamic model includes the different charge components associated with the tail states, deep states, interfaces, and traps and their associated time constants. Good agreement between modeling and experimental results is obtained.

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)0.001
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: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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