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Record W2763316469 · doi:10.1109/tcsii.2017.2759777

An Area-Efficient 8-Bit Single-Ended ADC With Extended Input Voltage Range

2017· article· en· W2763316469 on OpenAlexfundno aff
Simon Chaput, David Brooks, Gu-Yeon Wei

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2017
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsnot available
FundersDefense Advanced Research Projects AgencyFonds Québécois de la Recherche sur la Nature et les TechnologiesNational Science Foundation
KeywordsCapacitorCMOSSuccessive approximation ADC12-bitChipComputer scienceSwitched capacitorElectrical engineeringVoltageDynamic rangeRange (aeronautics)Electronic engineeringComputer hardwareEngineering

Abstract

fetched live from OpenAlex

This brief presents an 8-bit successive approximation register analog-to-digital converter (ADC) implemented within a system-on-chip (SoC) for autonomous flapping-wing microrobots. The ADC implements hybrid split-capacitor sub-digital-to-analog converter (DAC) techniques to achieve 35.72% improvement in a capacitor bank energy-area product. The device also implements an extended single-ended input voltage range allowing a direct connection to sensors while maintaining low-power operation. This technique allows 51.7% DAC switching energy reduction compared to the state of the art. The SoC, fabricated in 40-nm CMOS, includes four parallel 0.001 mm21 MS/s ADC cores multiplexed across 13 input ports. It enables 0 to 1.8-V input range while operating off of a 0.9 V supply. At 1 MS/s, the ADC achieves a signal-to-noise and distortion ratio of 45.6 dB for a 1.6-Vppinput signal and consumes 10.4 μW.

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 score0.004
Threshold uncertainty score0.014

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)0.0040.002

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 head0.026
GPT teacher head0.224
Teacher spread0.198 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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