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Record W3035520441 · doi:10.1109/tcsi.2020.2997778

A High-Voltage UWB Pulse Generator Using Passive Amplification in 65-nm CMOS

2020· article· en· W3035520441 on OpenAlexaff
Shengkai Gao, Kambiz Moez

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2020
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Alberta
FundersChina Scholarship Council
KeywordsAmplifierWidebandPulse generatorCMOSElectrical engineeringBandwidth (computing)Electronic engineeringWaveformVoltageComputer sciencePhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper proposes an ultra-wideband (UWB) pulse generator featuring high output amplitude for applications with long transmission and detection range. The output signal of a switch-mode power amplifier stage is passively amplified by a wideband matching network to produce output voltage amplitudes beyond the supply voltage level. Incorporating the parallel LC load of the power amplifier into the matching network and considering the constraints of the wideband matching when the power amplifier is ON and OFF, only a 2-section matching network is added for wideband matching. Employing a trapezoidal waveform as the input signal of the power amplifier, the design flexibility of the matching network to generate a regulation-compliant UWB pulse is further extended. Implemented in a 1-V 65-nm CMOS process, the proposed UWB pulse generator produces a UWB pulse with a de-embedded peak-to-peak amplitude (Vpp) of 2.49 V, a bandwidth of 6.8 GHz and pulse duration of 0.5 ns achieving 249% peak-to-peak to supply voltage ratio.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

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.

Opus teacher head0.027
GPT teacher head0.209
Teacher spread0.182 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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