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Record W1981891820 · doi:10.1063/1.2902499

Analysis of the off current in nanocrystalline silicon bottom-gate thin-film transistors

2008· article· en· W1981891820 on OpenAlexafffund
Mohammad R. Esmaeili-Rad, Andrei Sazonov, Arokia Nathan

Bibliographic record

VenueJournal of Applied Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPassivationMaterials scienceSilicon nitrideBand bendingNanocrystalline materialOptoelectronicsNitrideNanocrystalline siliconThin-film transistorConductivityTransistorAmorphous siliconSiliconCrystalline siliconLayer (electronics)NanotechnologyElectrical engineeringVoltageChemistry

Abstract

fetched live from OpenAlex

The off current in bottom-gate nanocrystalline silicon (nc-Si) thin-film transistor (TFT) is shown to be determined by the conductivity of the channel layer and by the quality of the interface with the passivation nitride. Indeed, the presence of fixed charges at the nc-Si∕passivation nitride interface serves to increase the band bending, leading to an increase in the off current by about two orders of magnitude. In contrast, when the nc-Si channel layer is capped with hydrogenated amorphous silicon (a-Si:H), the off current decreases and is determined by the bulk conductivity of nc-Si, as the a-Si:H makes a less defective interface with the passivation nitride. The general effect of the gate and passivation nitride interfaces on band bending and transfer characteristics of the TFT is analyzed by numerical simulations. We find that the band bending due to fixed charges at the gate nitride interface is modulated by a negative gate voltage, while that due to fixed charges at the passivation nitride interface is not, leading to a high off current.

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 score0.478
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.208
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

Citations17
Published2008
Admission routes2
Has abstractyes

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