Bidirectional Peripheral Nerve Interface With 64 Second-Order Opamp-Less ΔΣ ADCs and Fully Integrated Wireless Power/Data Transmission
Bibliographic record
Abstract
An active probe and microstimulator SoC for interfacing with peripheral nerves is presented. It performs 64-channel artifact-tolerant neural recording, cuff imbalance compensation by impedance sensing, and neurostimulation for the closed-loop operation. Each recording channel is a second-order opamp-less$\Delta \Sigma $ADC that consumes 140 nW and occupies 0.01 mm2area in 130 nm CMOS. The single-loop$\Delta \Sigma $architecture achieves second-order noise shaping with two passive integrators. To the best of our knowledge, this yields the lowest power and area of any second-order$\Delta \Sigma $ADC and the lowest FOM (fJ/conv. step) of any passive second-order$\Delta \Sigma $ADC (27 fJ/conv. step). The SoC uniquely performs multi-modal input signal recording: voltage (for neural recording) and current (for impedance sensing) are measured concurrently using frequency multiplexing. The SoC also features a 60 MHz energy-efficient inductive powering link and a 600 MHz RF data communication link. The prototype is validatedin vivoin the rat sciatic nerve for electroneurogram (ENG) sensing and the correction of impedance-imbalance in cuff electrodes.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".