High-voltage DC/DC converter for high-efficiency power recovery in implantable devices
Bibliographic record
Abstract
Implantable biomedical devices such as sensors and neurostimulators require a near-field inductive link to transmit power wirelessly. However, the near-field induced voltage is usually much larger than the compliance of low-voltage integrated circuit technologies. Thus most integrated power recovery approaches limit the induced signal to low-voltages with inefficient shunt regulation, or voltage clipping. We propose using a high-voltage (HV) CMOS technology to fully integrate the inductive power recovery front-end while adopting a step-down approach where the induced signal is limited to a much higher voltage (20 V). We previously reported a first IC that includes a HV rectifier and a HV regulator, which provide up to 12 V regulated DC supply from a 20 V maximum AC input. In this paper, we report the design of a second HV custom IC that completes the front-end by integrating an adjustable step-down switched capacitor DC/DC converter (1:3, 1:2 or 2:3 ratio). The IC has been submitted for fabrication in DALSA-C08E technology and the total silicon area including pads is 9mm2. Post-layout simulation results show that the DC/DC converter achieves more than 90 % power efficiency while providing about 3.9 V output with 12 V input, 1 mA load, 1:3 conversion ratio, and 50 kHz switching frequency.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".