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Record W4362690101 · doi:10.1109/jmw.2023.3261901

Near-Field Microwave Microscopy for 3D Surface Assessment of Manufactured Structures

2023· article· en· W4362690101 on OpenAlexaff
D. R. Jones, Phill Brown, G. McFarland, R. M. Perks, Heungjae Choi, Steve Cripps, Adrian Porch

Bibliographic record

VenueIEEE Journal of Microwaves · 2023
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsMiller Group (Canada)
Fundersnot available
KeywordsMetrologyMicrowaveCoaxialCapacitanceDielectricMaterials scienceSurface metrologyField (mathematics)MicroscopySystem of measurementOptoelectronicsOpticsElectrical engineeringComputer scienceEngineeringPhysicsElectrodeTelecommunicationsComposite materialProfilometerSurface finish

Abstract

fetched live from OpenAlex

Using near-field scanning microwave microscopy as a contact and non-contacting investigative tool for 3D surface metrology with three differing measurement modes, it has been possible to analyse structures that may be difficult for existing metrology systems. The system utilizes the small change in capacitance between a coaxial resonant probe (at around 2 GHz) ending in an open circuit tip, and the sample surface. This is measured in the frequency domain by the shift in the resonance frequency of the voltage transmission coefficient |S21|. It is also possible to investigate various materials (metallics, plastics etc.) owing to their differing dielectric properties. The probe has been tested on a computer-controlled 3D stage but is suitable for incorporation into a commercial co-ordinate measurement machine (CMM) to enhance its capability to inspect the inside surfaces of structures, e.g., threads in small bores.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.300
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

Explore more

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