Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".