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Record W2009548768 · doi:10.1116/1.2194932

Switching-speed calculations for Schottky-barrier carbon nanotube field-effect transistors

2006· article· en· W2009548768 on OpenAlexaff
D.L. John, D.L. Pulfrey

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCarbon nanotube field-effect transistorMaterials scienceCarbon nanotubeTransistorSchottky barrierOptoelectronicsInsulator (electricity)NanotubeField-effect transistorFigure of meritNanotechnologyElectrical engineeringVoltageEngineering

Abstract

fetched live from OpenAlex

The switching times and on/off-current ratios are computed for Schottky-barrier carbon nanotube field-effect transistors with different tube diameters and insulator thicknesses. It is indicated that it may be difficult to obtain a device exhibiting both high speed and low leakage current. A small-diameter nanotube with a thin insulator may offer the best compromise. It is also demonstrated that interelectrode capacitances can be large, thereby calling into question the usefulness of the intrinsic switching time as a figure of merit for transistors intended for digital-logic applications. The extrinsic switching time is a more appropriate metric and it is shown here that considerable optimization of the carbon nanotube field-effect transistor will be required to achieve figures better than for modern Si complementary metal-oxide-semiconductor transistors over a reasonable range of on/off-current ratio.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.247
Teacher spread0.242 · 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 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
Published2006
Admission routes1
Has abstractyes

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Same venueJournal of Vacuum Science & Technology A Vacuum Surfaces and FilmsSame topicCarbon Nanotubes in CompositesFrench-language works237,207