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Record W2310435968 · doi:10.1109/tvlsi.2015.2454448

A Stagger-Tuned Transimpedance Amplifier

2015· article· en· W2310435968 on OpenAlexafffund
Mohammad Hossein Taghavi, Peyman Ahmadi, Leonid Belostotski, J.W. Haslett

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Technology FuturesCMC Microsystems
KeywordsTransimpedance amplifierCMOSPhotodiodeBandwidth (computing)AmplifierCapacitanceElectrical engineeringPassbandElectronic engineeringPhysicsOptoelectronicsComputer scienceDifferential amplifierEngineeringTelecommunicationsBand-pass filter

Abstract

fetched live from OpenAlex

A new transimpedance amplifier (TIA) design procedure using stagger tuning with inverted transformer coils is described in this paper. A broadband TIA, realized using the proposed staggered design technique that enhances the transimpedance limit and the bandwidth while only adding small passband gain ripple, was implemented in a 0.13-μm standard CMOS process. The TIA achieves a 3-dB bandwidth of 33 GHz with a 150 fF photodiode capacitance. The TIA transimpedance gain is 43.8 dBQ with ±8 ps group-delay variation over the entire bandwidth. The circuit occupies an active area of 250 μm × 260 μm and consumes 9 mW from a 2 V supply. Despite operating with much larger photodiode capacitance, the TIA achieves the highest figure of merit, and occupies smaller area while consuming the least amount of power among previously published TIAs designed for the same data rate in similar technologies.

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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.234
Teacher spread0.207 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Very Large Scale Integration (VLSI) SystemsSame topicRadio Frequency Integrated Circuit DesignFrench-language works237,207