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Record W2760633551 · doi:10.1109/lmwc.2017.2745490

<italic>In Situ</italic> Test of Thickness and Sheet Resistance of Conductive Nanomaterial Using Microwave Cavity

2017· article· en· W2760633551 on OpenAlexaff
Ming Ye, Lu Wang, Yongning He, Mojgan Daneshmand

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicrowaveElectrical conductorSheet resistanceMaterials scienceIn situElectrical engineeringEngineeringNanotechnologyComposite materialTelecommunicationsChemistry

Abstract

fetched live from OpenAlex

Both thickness and sheet resistance of conductive nanomaterial deposited on glass substrate have been simultaneously measured using the TE011 mode cavity method. Measurements have been performed in two cavities with resonant frequency of ~10.8 and ~17.7 GHz, respectively. Measurement errors of thickness and sheet resistance can reach below 10%. Compared to existing methods, the presented method does not require prior knowledge on either film thickness or conductivity; what is more, by using the film as one of the end plates of the cavity, this method is noncontact and has little requirements on sample shape. These advantages suggest that the presented method has potential to be used in situ of monitoring the metal film characteristics such as that in microelectronic fabrication industry.

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)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.001

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 head0.025
GPT teacher head0.231
Teacher spread0.205 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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