An Ultra Compact and Fully-Integrated Tunable IR-UWB Transmitter in 28 nm CMOS for High-Density Neural Implants
Bibliographic record
Abstract
This paper presents a fully-CMOS, tunable impulse radio ultra-wideband (IR-UWB) transmitter for high-density implantable neural recording systems. Fabricated in 28 nm CMOS technology, the transmitter features an ultra-compact edge-combining architecture based on a digitally tunable impulse response filter (IRF) occupying only 0.0027 mm 2. The measured output exhibits a central frequency of 4.5 GHz with a 10-dB bandwidth of 3.6 GHz, providing robust spectral performance. It achieves 1 GHz frequency tunability by adjusting pulse widths from 650 ps to 800 ps, ensuring compliance with FCC spectral masks. A current-starved ring VCO (CSRVCO), with a compact 150 µm 2 layout, serves as the clock source, offering wide frequency tuning, low power consumption, and enhanced output power. The transmitter employs On-Off Keying (OOK) modulation and delivers a peak output amplitude of 660 mV with up to 5.1% energy efficiency. At a 20 MHz pulse repetition rate, the transmitter consumes only 78 µW while producing −24.5 dBm of output power. Measurement results confirm compliance with FCC regulations and suitability for deeply implanted biomedical applications. The design achieves a competitive figure of merit (FoM) of 0.017 (mm 2 • pJ)/(b • V), demonstrating its scalability and efficiency compared to prior state-of-the-art solutions.
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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".