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Record W4309001747 · doi:10.3390/electronics11223719

Resolving the Unusual Gate Leakage Currents of Thin-Film Transistors with Single-Walled Carbon-Nanotube-Based Active Layers

2022· article· en· W4309001747 on OpenAlexafffund
Sean F. Romanuik, Bishakh Rout, Pierre‐Luc Girard‐Lauriault, Sharmistha Bhadra

Bibliographic record

VenueElectronics · 2022
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceThin-film transistorLeakage (economics)Carbon nanotubeOptoelectronicsActive layerTransistorGate oxideFabricationElectrodeThreshold voltageGate dielectricLayer (electronics)DielectricVoltageNanotechnologyElectrical engineering

Abstract

fetched live from OpenAlex

Solution-processed single-walled carbon nanotube (SWCNT) thin-film transistors (TFTs) in the research stage often have large active areas. This results in unusual gate leakage currents with high magnitudes that vary with applied voltages. In this paper, we report an improved structure for solution-processed SWCNT-based TFTs. The unusual gate leakage current in the improved structure is resolved by patterning the SWCNT active layer to confine it to the channel region. For comparative purposes, this improved structure is compared to a traditional structure whose unpatterned SWCNT active layer expands well beyond the channel region. As TFT performance also varies with oxide layer thickness, 90 nm and 300 nm thick oxides were considered. The improved TFTs have gate leakage currents far lower than the traditional TFT with the same dimensions (aside from the unpatterned active area). Moreover, the unusual variation in gate leakage current with applied voltages is resolved. Patterning the SWCNT layer, increasing the oxide thickness, and reducing the top electrode length all help prevent a rapid dielectric breakdown. To take advantage of solution-based fabrication processes, the active layer and electrodes of our TFTs were fabricated with solution-based depositions. The performance of the TFT can be further improved in the future by increasing SWCNT solution incubation time and reducing channel size.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
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
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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.

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

Citations3
Published2022
Admission routes2
Has abstractyes

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