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Record W4407051591 · doi:10.1109/tcsii.2025.3537617

A 400-Mbps 1.05 pJ/Bit IR-UWB Transmitter for High-Density Neural Recording Systems

2025· article· en· W4407051591 on OpenAlexafffund
Hadi Hayati, Saeed Ghaneei Aarani, Mousa Karimi, Razieh Eskandari, Mohamad Sawan, Gabriel Gagnon-Turcotte, Benoit Gosselin

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2025
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsCMC Microsystems (Canada)Université Laval
FundersNatural Sciences and Engineering Research Council of CanadaWeston Family Foundation
KeywordsTransmitterBit (key)Computer scienceNeural systemElectronic engineeringTelecommunicationsNeuroscienceEngineeringPsychologyComputer network

Abstract

fetched live from OpenAlex

This brief presents an impulse radio ultra-wideband (IR-UWB) transmitter (TX) designed for miniature multi-channel neural recording platforms in freely moving laboratory animals. We introduce a 5-bit data-to-time modulation technique utilizing a 5-cell capacitive array. This approach minimizes the data rate’s dependency on pulse repetition frequency, while significantly reducing power dissipation and simplifying the TX’s architecture. Measurement results with the TX fabricated in TSMC 65-nm standard CMOS technology show that the proposed circuit provides a linear time change in the pulsewidth with a time step of 84 ps in average for every least significant bit in the input. Furthermore, the entire circuit consumes only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$422~{\mu }$ </tex-math></inline-formula>W from a 0.65-V supply. The proposed TX achieves a significantly low energy consumption of 1.05 pJ/bit at 400 Mbps in a 0.5-m transmission range. The fabricated circuit occupies 0.255 mm2 of die area including pads.

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)0.001
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: none
Teacher disagreement score0.965
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

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