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Record W1992389990 · doi:10.1109/iscas.2012.6271739

Low-power high-voltage charge pumps for implantable microstimulators

2012· article· en· W1992389990 on OpenAlexafffund
Goutam Chandra Kar, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC Microsystems
KeywordsCMOSElectrical engineeringVoltageSwitched-mode power supplyVoltage optimisationLow voltagePower (physics)CPU core voltageSwitched-mode power supply applicationsLow-power electronicsConstant power circuitCapacitive power supplyEngineeringHigh voltageCharge pumpElectronic engineeringPower consumptionCapacitorPhysics

Abstract

fetched live from OpenAlex

Two low-power high-voltage charge pumps have been designed to generate required voltage needed for intracortical microstimulation. High-voltage technology (0.8μm CMOS) has been used to generate high-supply voltage level with low-power consumption and low-voltage technology (0.13μm CMOS) has been used to investigate the possibility of generating high-supply voltage level overcoming the technology limitations. Both designs generate supply voltages more than 20V (>; ±10V) and consume low static power. The design in 0.8μm technology generates 24.52V supply voltage for no load and consumes 3.73mW static power. On the other hand, the design in 0.13μm CMOS technology generates 18.96 V for no load and consumes 0.8976mW static power.

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 score0.005
Threshold uncertainty score0.016

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 integrity0.0010.001
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 head0.023
GPT teacher head0.260
Teacher spread0.236 · 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
Published2012
Admission routes2
Has abstractyes

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