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A 180 nm CMOS Low-Power Digitally Controlled Charge-Pump for CMUT Devices Generating up to 23V

2024· article· en· W4402557786 on OpenAlexaff
Safa Razavinejad, Seyedfakhreddin Nabavi, Frédéric Nabki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsCMOSCharge pumpElectrical engineeringMaterials sciencePower (physics)OptoelectronicsLow-power electronicsCapacitive micromachined ultrasonic transducersUltra low powerElectronic engineeringCapacitorEngineeringCapacitive sensingVoltagePhysicsPower consumption

Abstract

fetched live from OpenAlex

The operation of capacitive micromechanical ultrasonic transducers (CMUT) depends entirely on a high bias voltage, and this voltage needs to be adjusted according to the physical aspects of CMUTs. Hence, this paper proposes a digitally controlled charge pump circuitry designed and implemented using TSMC 180 nm CMOS technology. The system comprises an 11 -stage cross-coupled charge pump, and can provide a variable output voltage within the range of 15 V to 23 V at an input voltage of 3.3 V. This is achieved through precise digitally control of the frequency clock of charge pump from 65 MHz to 105 MHz. As such, the proposed system ensures adaptability to diverse operational requirements. Through a comprehensive schematic and post-layout simulations it is shown that the entire system has a low power consumption of 48.2 µW at a supply voltage of 3.3V, and occupies an area of 0.09 mm2.

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 score0.005
Threshold uncertainty score0.016

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)0.0050.001

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 head0.012
GPT teacher head0.228
Teacher spread0.217 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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