Efficient and Easy to Fabricate Silicon-Based Planar Micro-Coils for Wireless Power Transfer Applications
Bibliographic record
Abstract
Wireless power transfer (WPT) has been recognized as a feasible solution to expand the lifespan of wearable and implants. This study proposes two topologies for planar micro-coils, namely, non-spiral and quasi-spiral, for WPT applications. The proposed micro-coils require a simple microfabrication process, due to the positioning of both terminals outside the coil loops at the same layer. Advantageously, by using a single mask microfabrication process the micro-coils can be fabricated on the silicon substrate. By simulation, we demonstrate that in the non-spiral topology the electrical potential and magnetic flux density distribute uniformly. For comparison purposes, a commercial circular wound coil which occupies 4.35 times higher volume in a circuit than the fabricated micro-coils is employed. Experimental results indicate that the non-spiral and quasi-spiral micro-coils wirelessly receive 3.6 and 1.3 times higher power, respectively, than the commercial coil. This efficiency is not confined to the amount of the scavenged power, while the micro-coils can effectively and efficiently charge a super-capacitor in a shorter period. Furthermore, the capability of the micro-coils to re- charge battery of a vital sign monitoring wearable is demonstrated. The ability to integrate the micro-coils in wearables and implantable medical devices will enable body-worn or implanted systems to be powered or recharged wirelessly while keeping their size miniaturized.
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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 | 0.001 |
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.
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".