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Record W2038891840 · doi:10.1142/s0129156406004090

ELECTROSTATICS OF NANOWIRES AND NANOTUBES: APPLICATION FOR FIELD–EFFECT DEVICES

2006· article· en· W2038891840 on OpenAlexaff
A. Shik, Harry E. Ruda, Slava V. Rotkin

Bibliographic record

VenueInternational Journal of High Speed Electronics and Systems · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanowireElectrostaticsCarbon nanotubeField-effect transistorSemiconductorNanotubeTransistorCharge (physics)DiffusionChannel (broadcasting)HysteresisCondensed matter physicsField (mathematics)PhysicsMaterials sciencePlanarNanotechnologyOptoelectronicsVoltageQuantum mechanicsElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

We present a quantum and classical theory of electronic devices with one–dimensional (1D) channels made of a single carbon nanotube or a semiconductor nanowire. An essential component of the device theory is a self–consistent model for electrostatics of 1D systems. It is demonstrated that specific screening properties of 1D wires result in a charge distribution in the channel different from that in bulk devices. The drift–diffusion model has been applied for studying transport in a long channel 1D field–effect transistor. A unified self–consistent description is given for both a semiconductor nanowire and a single–wall nanotube. Within this basic model we analytically calculate equilibrium (at zero current) and quasi–equilibrium (at small current) charge distributions in the channel. Numerical results are presented for arbitrary values of the driving current. General analytic expressions, found for basic device characteristic, differ from equations for a standard bulk three–dimensional field–effect device. The device characteristics are shown to be sensitive to the gate and leads geometry and are analyzed separately for bulk, planar and quasi–1D contacts. The basic model is generalized to take into account external charges which can be polarized and/or moving near the channel. These charges change the self–consistent potential profile in the channel and may show up in device properties, for instance, a hysteresis may develop which can have a memory application.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.252
Teacher spread0.248 · 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 designBench or experimental
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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of High Speed Electronics and SystemsSame topicCarbon Nanotubes in CompositesFrench-language works237,207