Toward A Fully Integrated Neurostimulator With Inductive Power Recovery Front-End
Bibliographic record
Abstract
In order to investigate new neurostimulation strategies for micturition recovery in spinal cord injured patients, custom implantable stimulators are required to carry-on chronic animal experiments. However, higher integration of the neurostimulator becomes increasingly necessary for miniaturization purposes, power consumption reduction, and for increasing the number of stimulation channels. As a first step towards total integration, we present in this paper the design of a highly-integrated neurostimulator that can be assembled on a 21-mm diameter printed circuit board. The prototype is based on three custom integrated circuits fabricated in High-Voltage (HV) CMOS technology, and a low-power small-scale commercially available FPGA. Using a step-down approach where the inductive voltage is left free up to 20 V, the inductive power and data recovery front-end is fully integrated. In particular, the front-end includes a bridge rectifier, a 20-V voltage limiter, an adjustable series regulator (5 to 12 V), a switched-capacitor step-down DC/DC converter (1:3, 1:2, or 2:3 ratio), as well as data recovery. Measurements show that the DC/DC converter achieves more than 86% power efficiency while providing around 3.9-V from a 12-V input at 1-mA load, 1:3 conversion ratio, and 50-kHz switching frequency. With such efficiency, the proposed step-down inductive power recovery topology is more advantageous than its conventional step-up counterpart. Experimental results confirm good overall functionality of the system.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".