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Record W2091556826 · doi:10.1109/tmag.2013.2240276

Giant Magneto-Impedance Thin Film Magnetic Sensor

2013· article· en· W2091556826 on OpenAlexaff
Saman Nazari Nejad, Arash A. Fomani, Raafat R. Mansour

Bibliographic record

VenueIEEE Transactions on Magnetics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceElectrical impedanceMagnetic fieldWaferFabricationThin filmOptoelectronicsNuclear magnetic resonanceAnalytical Chemistry (journal)NanotechnologyElectrical engineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

A thin film sensor for sensing magnetic fields bellow 10 Gausses is designed and fabricated based on a giant magneto-impedance (GMI) structure. Analytical equations describing GMI effect have been employed to design the sensor over the designated magnetic field and signal frequency. The GMI multilayer is comprised of an Au layer (200 nm) sandwiched between two Co73Si12B15magnetic layers (400 nm). Various structures with different shapes and ratios have been studied to achieve the optimum performance. The sensors are fabricated on a glass wafer employing thin-film micro-fabrication processes. The paper also presents a new post-processing step to magnetize the GMI multilayer. The impedance of the GMI sensors as a function of external magnetic and excitation frequency is reported and discussed. The sensor has been utilized to change the state of an On/Off circuit in the presence/absence of magnetic field.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on MagneticsSame topicMagnetic properties of thin filmsFrench-language works237,207