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Record W2074689931 · doi:10.1109/lmwc.2015.2421253

A CMOS Low-Power Cross-Coupled Immittance-Converter Transimpedance Amplifier

2015· article· en· W2074689931 on OpenAlexafffund
M. Hossein Taghavi, Leonid Belostotski, J.W. Haslett

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesCMC Microsystems
KeywordsTransimpedance amplifierImmittanceCMOSElectrical engineeringAmplifierCapacitancePhysicsPreamplifierImpedance matchingBandwidth (computing)OptoelectronicsMaterials scienceElectronic engineeringOperational amplifierEngineeringElectrical impedanceTelecommunicationsElectrode

Abstract

fetched live from OpenAlex

This letter presents a low-power transimpedance amplifier (TIA) that employs an immittance converter, which provides both a negative input resistance to increase the input pole frequency and a negative inductance to improve the circuit stability. These allow for a significant bandwidth enhancement with low power consumption. Two TIAs, TIA1 with 6 GHz and TIA2 with 8.8 GHz 3 dB bandwidths, were fabricated in a standard 0.13- μm CMOS technology and were designed to operate with a 250 fF photodiode capacitance at 10 Gb/s. The transimpedance gains of the single-stage TIAs, followed by near-unity-gain output-matching buffers, are ~ 54 dBΩ, the group-delay variations and average input-referred noise currents are ±3 ps and 24 pA/√{Hz} (TIA1) and ±70 ps and 28 pA/√{Hz} (TIA2) over their 3 dB bandwidths. The TIAs occupy an active area of 250 μm× 160 μm, and without the output-matching buffer each consumes 2 mW.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.220
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 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

Citations27
Published2015
Admission routes2
Has abstractyes

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