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Ultra-Compact Tunable IR-UWB Transmitter in 28 nm CMOS for Neural Implants with IEEE 802.15.6 Compliance

2025· preprint· en· W4412660097 on OpenAlexaff
Esmaeil Ranjbar Koleibi, Reza Bostani, Konin Koua, Sébastien Roy, Frédéric Nabki, Benoit Gosselin, Réjean Fontaine

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversité LavalÉcole de Technologie SupérieureUniversité de Sherbrooke
Fundersnot available
KeywordsTransmitterCMOSCompliance (psychology)Materials scienceWirelessOptoelectronicsElectrical engineeringComputer scienceTelecommunicationsEngineeringPsychologyChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper presents an ultra-compact, energy-efficient IR-UWB transmitter designed in 28 nm CMOS technology for high-density, wireless neural recording implants. The proposed architecture employs a tunable ring VCO-based pulse generator with a differential antenna driver, enabling wide frequency adaptability across lower and upper UWB bands while maintaining compliance with FCC, ECC, and Japanese spectrum masks. A startup-optimized pulse generation scheme and a high-efficiency output stage-both proposed for the first time in this work-enhance transient response and boost output amplitude. The transmitter achieves a peak-to-peak output voltage as high as 1.33 V with a 1.2 V supply, and delivers output power and energy efficiency up to −7.5 dBm and 3.7%, respectively. It supports IEEE 802.15.6 channels 1-9 with tunable bandwidth and occupies only 0.0019 mm 2 of silicon area. These features make it particularly suitable for deep cortical implants where high output amplitude and power efficiency are essential under stringent power and size constraints.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.255
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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