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Record W2049004495 · doi:10.1088/0957-4484/26/8/085204

Axial p–n-junctions in nanowires

2015· article· en· W2049004495 on OpenAlexafffund
Carlos Fernandes, A. Shik, Kristopher Byrne, David Lynall, M. Blumin, Igor Saveliev, Harry E. Ruda

Bibliographic record

VenueNanotechnology · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectric fieldMaterials scienceCondensed matter physicsNanowireElectronDrop (telecommunication)SemiconductorDiffusionVoltageBoundary value problemCharge carrierPhysicsNanotechnologyOptoelectronicsQuantum mechanicsElectrical engineering

Abstract

fetched live from OpenAlex

The charge distribution and potential profile of p-n-junctions in thin semiconductor nanowires (NWs) were analyzed. The characteristics of screening in one-dimensional systems result in a specific profile with large electric field at the boundary between the n- and p- regions, and long tails with a logarithmic drop in the potential and charge density. As a result of these tails, the junction properties depend sensitively on the geometry of external contacts and its capacity has an anomalously large value and frequency dispersion. In the presence of an external voltage, electrons and holes in the NWs can not be described by constant quasi-Fermi levels, due to small values of the average electric field, mobility, and lifetime of carriers. Thus, instead of the classical Sah-Noice-Shockley theory, the junction current-voltage characteristic was described by an alternative theory suitable for fast generation-recombination and slow diffusion-drift processes. For the non-uniform electric field in the junction, this theory predicts the forward branch of the characteristic to have a non-ideality factor η several times larger than the values 1 < η < 2 from classical theory. Such values of η have been experimentally observed by a number of researchers, as well as in the present work.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.380

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 head0.029
GPT teacher head0.243
Teacher spread0.214 · 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 designNot applicable
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

Citations4
Published2015
Admission routes2
Has abstractyes

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