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Record W2972074211 · doi:10.1021/acs.jpcc.9b06766

Origin of the Anomalous Electronic Shot Noise in Atomic-Scale Junctions

2019· article· en· W2972074211 on OpenAlexafffund
Anqi Mu, Ofir Shein-Lumbroso, Oren Tal, Dvira Segal

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Toronto
FundersIsrael Science FoundationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMinerva Foundation
KeywordsShot noiseAsymmetryCondensed matter physicsNoise (video)Ohmic contactPhysicsVoltage dropSchottky diodeQuantum noiseBiasingComputational physicsOptoelectronicsVoltageOpticsQuantumQuantum mechanicsDiodeElectrodeComputer scienceDetector

Abstract

fetched live from OpenAlex

Fluctuations pose fundamental limitations in making sensitive measurements, yet at the same time, noise unravels properties that are inaccessible at the level of the averaged signal. In electronic devices, shot noise arises from the discrete nature of charge carriers, and it increases linearly with averaged current (or applied bias for ohmic conductors) according to the celebrated Schottky formula. Nonetheless, measurements of shot noise in atomic-scale junctions at high voltage reveal significant nonlinear (anomalous) behavior, which varies from sample to sample, and has no specific trend. Here, we provide a viable, unifying explanation for these diverse observations based on the theory of quantum coherent transport. Our formula for the anomalous shot noise relies on—and allows us to resolve—two key characteristics of a conducting junction: The structure of the transmission function at the vicinity of the Fermi energy and the asymmetry of the bias voltage drop at the contacts. We test our theory on high voltage shot noise measurements on Au atomic scale junctions and demonstrate a quantitative agreement, recovering both the enhancement and suppression of shot noise as observed in different junctions. The good theory–experiment correspondence supports our modeling and emphasizes that the asymmetry of the bias drop on the contacts is a key factor in nanoscale electronic transport, which may substantially impact electronic signals even in incomplex structures.

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 score0.030
Threshold uncertainty score0.271

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 integrity0.0000.001
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 head0.216
Teacher spread0.211 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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