An Analog Low-Power Highly-Accurate Link-Adaptive Energy Storage Efficiency Maximizer for Resonant CM Wireless Power Receivers
Bibliographic record
Abstract
Summary form only given. The power delivered wirelessly to implantable neural interfaces supplies two categories of loads with distinct consumption patterns: small-and-continuous (e.g., recording circuits, signal conditioning) or large-and-intermittent (e.g., electrical stimulation, wireless transmission). For the weakly-coupled mm-scale implants, the induced power level at the receiver coil (Rx) is typically far below the required instantaneous power of the large-intermittent loads. Therefore, these loads could only be supplied through storage of excess incoming energy during their off cycles. As such, the energy storage efficiency determines how often and how powerful a high-power event (e.g., data transmission, stimulation) could take place. Motivated by this, a variety of circuit ideas for energy delivery optimization are reported, mostly focused on current-mode (CM) receivers, mainly due to their superior performance in weakly-coupled links (compared to voltage-mode receivers). However, the optimization is either done only for resistive loads (i.e., not optimizing storage efficiency), or done pre-operation (i.e., offline), hence, not adaptive to link variations (e.g., implant movements, media changes, etc.). We present a low-power integrated circuit (IC) that senses the peak voltage at the Rx coil (VRX(peak)), calculates the optimal timing scheme for maximum energy storage efficiency in real time, and operates the CM receiver accordingly. This closed-loop scheme makes the presented work adaptive to any link variation and needless of calibration.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".