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Record W2197405953 · doi:10.1109/tcsii.2011.2149030

84-dB Range Logarithmic Digital-to-Analog Converter in CMOS 0.18- $\mu\hbox{m}$ Technology

2011· article· en· W2197405953 on OpenAlexafffund
Sandhya Purighalla, Brent Maundy

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2011
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Calgary
FundersCMC Microsystems
KeywordsCMOSLogarithmAnalog-to-digital converterDynamic rangeElectrical engineeringLog amplifierLinearityAmplifierDifferential nonlinearityRange (aeronautics)VoltageElectronic engineeringPhysicsOperational amplifierTopology (electrical circuits)Materials scienceMathematicsEngineeringMathematical analysis

Abstract

fetched live from OpenAlex

This brief presents an 84-dB dynamic range true logarithmic amplifier. Logarithmic ordering in the output is achieved as a function of control parameter X, which in turn is tuned digitally. The result is a digitally controlled data conversion that yields a new type of logarithmic digital-to-analog converter. This 4-bit nonlinear converter has been fabricated in a TSMC 0.18-μm complimentary metal-oxide-semiconductor technology. Measurement results confirm theory and simulations, and display logarithmic transfer characteristics within [-1.36%, 0.84%] output linear error. The integrated circuit has dimensions of 1.5 × 1 mm2and an average power consumption of 13.2 mW when powered with ±1.65-V supply voltages.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.003

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 head0.029
GPT teacher head0.207
Teacher spread0.178 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

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