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Record W2032099620 · doi:10.1088/0957-0233/18/8/l01

Sub-micron resolution magnetic force microscopy mapping of current paths with large probe-to-sample separation

2007· article· en· W2032099620 on OpenAlexaff
A. Pu, D. J. Thomson

Bibliographic record

VenueMeasurement Science and Technology · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMagnetic force microscopeResolution (logic)Materials scienceCurrent (fluid)Sample (material)MicroscopyScanning probe microscopySeparation (statistics)Atomic force microscopyHigh resolutionNon-contact atomic force microscopyOpticsConductive atomic force microscopyMagnetic fieldNanotechnologyPhysicsChemistryComputer scienceMagnetizationRemote sensingGeologyChromatographyArtificial intelligence

Abstract

fetched live from OpenAlex

The presence of thick over layers results in magnetic force having considerable signal-to-noise advantages over force gradient for imaging the stray magnetic fields generated by current-carrying conductors in integrated circuits. However, the longer interaction range of magnetic forces results in a considerable decrease in resolution due to coupling with the entire probe tip and cantilever. An extended model, which considers realistic magnetic force microscopy (MFM) probe geometries and the forces acting on the whole probe including along the cantilever of the probe, has been developed to show these effects. The results show that the cantilever contribution cannot be neglected. By using the difference between two images taken at different separations these effects can be largely eliminated and sub-micron resolution maps of conductor paths can be obtained at tip-to-conductor distances of greater than 2 µm.

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)0.000
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: none
Teacher disagreement score0.571
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.299
Teacher spread0.280 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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