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Record W2136020354 · doi:10.1109/tmag.2015.2388677

Fractal Loop Inductors

2015· article· en· W2136020354 on OpenAlexaff
Gem Shoute, Douglas W. Barlage

Bibliographic record

VenueIEEE Transactions on Magnetics · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFractalInductanceInductorEquivalent series resistanceTopology (electrical circuits)Loop (graph theory)Materials sciencePlanarPhysicsComputer scienceMathematical analysisMathematicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

We report a significant fractal-scaled increase in inductance for conductive pathways oriented in a fractal loop-based layout for a suite of planar inductors, which effectively occupy the same layout space and require only a single fabrication layer. These are closely predicted using a fractal scaling model for generalized loop-based inductors elucidated in this paper. Single-layer planar fractal structures were investigated with the loop inductor serving as the base construct. Thin sub-200 nm Cr/Au metal films compatible with flexible and stretchable substrates were used. It was found that while higher fractal orders did improve the inductive performance (over nine times from zeroth to third order), it is met with increased resistance. However, when compared with its equivalent series orientation, the effective sheet resistance of the simple fractal strategy demonstrated a clear advantage of up to four times. When the inductor is normalized for thickness, a quality factor Q greater than 40 is observed for all structures. Finally, the inductance quality gain figure of merit showing the optimal geometrical approach is introduced to quantify the quality of the structure's inductance over its resistance.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.551

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 head0.029
GPT teacher head0.233
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations15
Published2015
Admission routes1
Has abstractyes

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