Fully implantable, multi‐channel microstimulator with tracking supply ribbon, multi‐output charge pump and energy recovery
Bibliographic record
Abstract
Abstract A novel energy‐efficient approach dedicated to high‐density implantable stimulators such as visual prostheses is presented. Energy efficiency of the approach proposed in this work is achieved through two ideas: the ‘tracking supply ribbon’ technique and ‘reverse charge pumping’. The proposed approach is implemented, in the multi‐channel case, in such a way that power efficiency of each stimulation channel is enhanced according to its specific voltage/current condition and independently from other channels. For this purpose, a multi‐channel power‐efficient charge pump circuit with small integrated capacitors is proposed. Based on the proposed approach, a fully integrated 16‐channel stimulation backend for a visual prosthesis was designed and simulated in the transistor level in a standard 0.18‐μm triple‐well CMOS technology, occupying 1.41 mm 2 of silicon area. According to post‐layout simulation results, power savings of up to 74% for a single channel and 81.5% for multiple channels are achieved compared to the conventional output stage with a constant supply voltage. Designed for the proposed stimulation backend, the charge pump generates output voltages of 3.48 V, −1.69 V, −3.38 V, and −5.05 V out of a 1.8 V input voltage and exhibits average power efficiency of 92.8% and 86.8% for one‐ and three‐stage circuits, respectively, all in the case of a 100 μA current load. All the aforementioned results are based on post‐layout simulation. Moreover, a proof‐of‐concept prototype was developed using off‐the‐shelf components in order to demonstrate the operation of the proposed tracking supply ribbon idea.
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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 | 0.001 | 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".