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Single-dopant band bending fluctuations in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>MoSe</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:math> measured with electrostatic force microscopy

2022· article· lv· W3203918559 on OpenAlexafffund
Megan Cowie, Rikke Plougmann, Zeno Schumacher, Peter Grütter

Bibliographic record

VenuePhysical Review Materials · 2022
Typearticle
Languagelv
FieldEngineering
TopicAdvancements in Semiconductor Devices and Circuit Design
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsDopantMaterials scienceBand bendingMetalSemiconductorDopingCondensed matter physicsAnalytical Chemistry (journal)OptoelectronicsPhysics

Abstract

fetched live from OpenAlex

In this paper, we experimentally demonstrate two-state fluctuations in a metal-insulator-semiconductor device formed out of a metallic atomic force microscopy tip, vacuum gap, and multilayer ${\mathrm{MoSe}}_{2}$ sample. We show that noise in this device is intrinsically bias dependent due to the bias-dependent surface potential and does not require that the frequency or magnitude of individual dopant fluctuations are themselves bias dependent. Finally, we measure spatial nonhomogeneities in band bending (charge reorganization) timescales.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.401
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0730.001

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 head0.261
Teacher spread0.237 · 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.

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

Citations5
Published2022
Admission routes2
Has abstractyes

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