Low-power high-speed wireless transceivers and antennas for large-scale neural implants
Bibliographic record
Abstract
Advancement in wireless and microsystems technology have ushered in new devices that can directly interface with the central nervous system for stimulating and/or monitoring neural circuitry. In this paper, we present the design of low-power CMOS integrated transceivers intended for utilization into large-scale multi-channel neural stimulating/monitoring implants. We discuss the design and the implementation of different modulation schemes and pulse shaping strategies within CMOS circuits, we review the most critical design challenges of this sensitive application, we compare different solutions and circuit topologies in terms of performance and safety, and we introduce a suitable implantable UWB antenna. In particular, we present an integrated transmitter (TX) and a receiver (RX) that are designed to share a single implantable antenna. The TX generates ultra wideband (UWB) impulses based on edge combining, and the RX uses a low-power ISM-2.4-GHz narrow-band OOK receiver topology. The RX can support downlink telemetry of neural stimulation applications with a data rate as high as 100 Mbps within a power budget of 5 mW, while the TX is designed to support uplink back telemetry with a data rate of up to 800 Mbps for power consumption of 5.36 mW for BPSK modulation. Finally, we present measurement results obtained with biological tissues that confirm the full functionality of the fabricated implantable transceiver.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".