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Record W2316209074 · doi:10.1109/nano.2014.6967968

Characterization of nano Schottky junctions for a new structure of nano-electronic devices

2014· article· en· W2316209074 on OpenAlexaff
Moh’d Rezeq, Khouloud Eledlebi, Mohamed Ismail, Bo Cui, Ripon Kumar Dey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSchottky diodeMaterials scienceRectificationSchottky barrierOptoelectronicsNano-SiliconQuantum tunnellingMetal–semiconductor junctionDiodeSemiconductorNanotechnologySemiconductor deviceVoltageElectrical engineeringLayer (electronics)

Abstract

fetched live from OpenAlex

There is an increasing interest in reducing the size of semiconductor devices to sub 20 nm scale for technical requirements, like low power consumption and high switching speed. Electronic devices based on nano Schottky junctions have the potential to address these issues. This is because nano metal-semiconductor contacts are expected to have narrower barriers compared to conventional Schottky diodes. Nano Schottky junctions have been investigated experimentally using gold (Au) coated AFM tips in contact with different silicon (Si) substrates. For nano-tips with an apex radius around 7 nm, the current-voltage (I-V) curves on low n-dope Si substrates have showed a reversed rectification diode behavior compared to the high n-dope Si samples. We have used a new theoretical model to study the electric field enhancement at the nano metal-semiconductor interface, and thus the enhancement of the tunneling current. We have found out that the tunneling current at the reverse bias is dominant on low dope substrates and very small on high dope substrates. This accounts for the reversed I-V rectification behavior on low dope Si Schottky contacts. The calculated I-V curves showed good agreement with the experimental results for both types of Si samples.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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