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Record W2012101235 · doi:10.1063/1.4870466

Photoconductive response of strained silicon nanowires: A Monte Carlo study

2014· article· en· W2012101235 on OpenAlexaff

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhotoconductivityNanowirePhotocurrentGermaniumSiliconMonte Carlo methodPhotodiodeLight intensityWavelengthPhonon

Abstract

fetched live from OpenAlex

Using Ensemble Monte Carlo simulations, the photocurrent in a 500 nm long strained [110] silicon nanowire with diameter of 3.1 nm is investigated. It was observed that a phototransistor based on this nanowire can have responsivities in the order of 21.3 mA/W for an input light wavelength of 532 nm and intensity of 0.25–2.5 kW/cm2. The super-unity slope of 1.2 in photo conductance versus input light intensity suggests that the nanowire has a photoconductive gain and highlights its advantage over germanium nanowires with sub-unity slope (0.77). The generated photocurrents are in the 0.1 nA–1 nA range. Density Functional Theory and Tight Binding methods were used for strain application and band structure calculation, respectively. Both longitudinal acoustic and optical phonons were included in the calculation of the carrier-phonon scattering events, which showed a two-order of magnitude stronger role for longitudinal optical phonons.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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