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Record W2944423231 · doi:10.1039/c8nr10270h

Reduction of Fermi level pinning at Cu–BP interfaces by atomic passivation

2019· article· en· W2944423231 on OpenAlexafffund
Pengfei Ou, Xiao Zhou, Cheng Chen, Fanchao Meng, Yiqing Chen, Jun Song

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsMcGill University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPassivationReduction (mathematics)Materials scienceFermi levelCondensed matter physicsNanotechnologyPhysicsMathematicsQuantum mechanicsLayer (electronics)GeometryElectron

Abstract

fetched live from OpenAlex

Black phosphorus (BP) is a semiconducting material with a direct finite band gap in its monolayer, attracting intense attention for its application in field-effect transistors. However, strong Fermi level pinning (FLP) has been observed for contacts between BP and high work function metals, e.g., Cu. Such FLP presents an undesirable hurdle preventing the achievement of high performance field-effect devices. In this regard, there is a crucial need to understand the FLP occurring at the metal-BP interfaces and explore the possibility to reduce it. The present work studied atomic passivation in reducing FLP for the Cu-BP system using density functional theory calculations. The passivation by H, N, F, S, and Cl atoms on the Cu(111) surface has been considered. The results showed that the passivated atoms can shield the direct contact between Cu(111) and BP, thus reducing FLP at Cu-BP interfaces. In particular, S and Cl atoms were found to be highly effective agents to achieve a significant reduction of FLP, leading to Cu-BP contacts with ultralow Schottky barrier height (SBH) and suggesting the possibility of ohmic contact formation. Our findings demonstrate surface passivation as an effective method towards depinning the Fermi level at the metal-BP interface and subsequently controlling the SBH for BP-based electronic devices.

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 score0.003
Threshold uncertainty score0.412

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 head0.016
GPT teacher head0.222
Teacher spread0.206 · 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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