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Record W2611823112 · doi:10.1063/1.4982222

Space-charge-limited current in nanowires

2017· article· en· W2611823112 on OpenAlexafffund
S. Alagha, A. Shik, Harry E. Ruda, Igor Saveliev, K. L. Kavanagh, S. P. Watkins

Bibliographic record

VenueJournal of Applied Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsUniversity of TorontoSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsNanowireSpace chargeOhmic contactMaterials scienceSemiconductorCondensed matter physicsDepletion regionDopingCharge carrierNanotechnologyOptoelectronicsPhysicsElectronQuantum mechanics

Abstract

fetched live from OpenAlex

Space-charge-limited current is often observed in semiconductor nanowires due to carrier depletion and reduced electrostatic screening. We present a numerical study on geometric scaling of the space-charge-limited current in nanowires, in comparison with the thin film and bulk geometries, using an n+-n-n+-model. The model highlights the effects of the surroundings for thin films and nanowires and shows that the dielectric properties of the semiconductor have a negligible effect on the space-charge-limited transport for small dimensions. The distribution of equilibrium and injected charge concentration vary as the semiconductor dimensionality is reduced. For low doping, the ohmic current is controlled by charge diffusion from degenerate contacts rather than by the nanowire impurity concentration. The results of numerical calculations agree with a simple capacitance formalism which assumes a uniform charge distribution along the nanowire, and experimental measurements for InAs nanowires confirm these results. The numerical model also predicts that an asymmetric nanowire contact geometry can enhance or limit charge injection.

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 score0.033
Threshold uncertainty score0.344

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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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

Citations32
Published2017
Admission routes2
Has abstractyes

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