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Record W2132528324 · doi:10.1109/newcas.2012.6329049

A bandwidth enhancement technique for CMOS TIAs driven by large photodiodes

2012· article· en· W2132528324 on OpenAlexafffund
M. Hossein Taghavi, Leonid Belostotski, J.W. Haslett

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsTransimpedance amplifierPhotodiodeBandwidth (computing)CMOSCapacitanceOptoelectronicsRippleAmplifierElectronic engineeringComputer scienceMaterials scienceElectrical engineeringOperational amplifierPhysicsEngineeringTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

A new method of enhancing the bandwidth of a conventional series-peaked transimpedance amplifier (TIA) driven by a large photodiode is introduced. It is shown that by using N identical TIAs in parallel the circuit bandwidth can be significantly improved. The advantage of the proposed technique is in its ability to provide large bandwidth enhancements in multi-GHz frequency ranges even when photodiode capacitance is large, without a noticeable increase in TIA core circuit area. This technique is supported by a design example simulated in a 0.13μm standard CMOS technology. Simulation results show a 3dB bandwidth of 26GHz with 0.5pF photodiode capacitance, a transimpedance gain of 51dBΩ and group delay of 33.5±4ps. The proposed technique shows an overall bandwidth enhancement ratio of 3.25 with less than 0.1dB gain ripple resulting in higher bandwidth enhancement than previously reported for large photodiode capacitances.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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
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

Citations9
Published2012
Admission routes2
Has abstractyes

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