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Square-Wave Modulated Damping in Transimpedance Amplifiers

2020· article· en· W3083029050 on OpenAlexaff
Pouria Aminfar, Glenn Cowan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsConcordia University
Fundersnot available
KeywordsTransimpedance amplifierDamping factorPhysicsAmplifierControl theory (sociology)Intersymbol interferenceQ factorSquare waveModulation (music)AcousticsVoltageOperational amplifierElectrical engineeringComputer scienceEngineeringOptoelectronicsCMOSResonatorChannel (broadcasting)Quantum mechanics

Abstract

fetched live from OpenAlex

A shunt-feedback transimpedance amplifier (SF- TIA) with a dynamic damping factor is presented. In the proposed SF-TIA, the damping factor switches between a low negative and high positive value synchronously with the incoming data which allows the fast response of low-damping factor while mitigating the intersymbol interference (ISI) associated with underdamped 2nd-order systems. The damping factor is modulated by adding a triode-region transistor with a rail-to-rail square-wave bias voltage across the outputs which changes the damping factor of the system between -0.15 and 2.5 each unit interval. The proposed TIA achieves more than twice the vertical eye opening and lower input referred noise compared to an optimized reference TIA as well as higher gain and lower input referred noise compared to a dynamic-damping TIA with sinusoidal damping-factor modulation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.189
Teacher spread0.149 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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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