A Low-Voltage CMOS Rectifier With On-Chip Matching Network and a Magnetic Field Focused Antenna for Wirelessly Powered Medical Implants
Bibliographic record
Abstract
In this work, we present a CMOS rectifier and its associated external transmitting antenna that are designed for wirelessly powered implantable devices in general, and for a smart medical stent interface, in particular. The detailed characterization and modelling procedures of the "smart stent'' implant are presented, and the extracted circuit model of the stent is used for stent-rectifier co-optimization. A fully on-chip transformer-based tunable matching network is co-designed with the differential cross-coupled rectifier. At the external side, a four-port driven antenna is designed to focus the magnetic field in tissue as well as enhance the power density around the implant. As a proof-of-concept, the rectifier is fabricated in a 0.13 μm CMOS process and the measurement results show that it can generate more than 500 mV DC voltage on a 2 kΩ load when the available power of the stent is greater than -2 dBm, corresponding to 34% power conversion efficiency (PCE). Finally, the "smart stent" system is tested in-vitro. The results of the wireless power transfer experiments show that with 480 mW transmitting power and 53 mm separation distance (including 33 mm phantom tissue), more than 350 μW is delivered to the rectifier's 2 kΩ load.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".