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Record W1601678564 · doi:10.1088/1367-2630/17/7/073023

Scanning tunneling spectroscopy reveals a silicon dangling bond charge state transition

2015· article· en· W1601678564 on OpenAlexafffund

Bibliographic record

VenueNew Journal of Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesWestern Canada Research Grid
KeywordsDangling bondDopantScanning tunneling spectroscopyScanning tunneling microscopeCharge (physics)SiliconQuantum tunnellingSpectroscopy

Abstract

fetched live from OpenAlex

We report the study of single dangling bonds (DBs) on a hydrogen-terminated silicon (100) surface using a low-temperature scanning tunneling microscope. By investigating samples prepared with different annealing temperatures, we establish the critical role of subsurface arsenic dopants on the DB electronic properties. We show that when the near-surface concentration of dopants is depleted as a result of 1250 °Cflash anneals, a single DB exhibits a sharp conduction step in its I(V) spectroscopy that is not due to a density of states effect but rather corresponds to a DB charge state transition. The voltage position of this transition is perfectly correlated with bias-dependent changes in the STM images of the DB at different charge states. Density functional theory calculations further highlight the role of subsurface dopants on DB properties by showing the influence of the DB-dopant distance on the DB state. We discuss possible theoretical models of electronic transport through the DB that could account for our experimental observations.

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 score0.379
Threshold uncertainty score0.705

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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

Citations50
Published2015
Admission routes2
Has abstractyes

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