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Record W2781492707 · doi:10.1109/jrfid.2018.2790360

An Ultra Low-Voltage Low-Power Capacitance-to-Digital Converter for Wirelessly Powered Intraocular Pressure Sensor

2017· article· en· W2781492707 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Journal of Radio Frequency Identification · 2017
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversity of Alberta
FundersCenters for Disease Control and PreventionCMC Microsystems
KeywordsElectrical engineeringAmplifierCapacitive sensingLow voltageSubthreshold conductionVoltageLow-power electronicsCapacitancePower (physics)EngineeringMaterials scienceElectronic engineeringCMOSPower consumptionPhysicsTransistorElectrode

Abstract

fetched live from OpenAlex

This paper presents the design and implementation of a low-power and low-voltage capacitance-to-digital converter (CDC) for a wirelessly powered intraocular pressure (IOP) sensor. To minimize power consumption and enable operation with ultra low voltages, a CDC is designed based on a ΔΣ modulation using the inverter amplifier operating in the deep-subthreshold region as integrator amplifiers. The CDC, implemented in 0.13 μm CMOS process and tested in a pressure chamber with MEMS capacitive sensors, obtains an integral nonlinearity of 2.1 mmHg (corresponding to 1.25 fF) over a range of 0-70 mmHg above ambient only consuming 30 nW from a 325-mV supply. This paper presents the highest reported capacitive resolution, the lowest reported power and operating voltage for CDC used with IOP measurement, obtaining a 4.5× reduction in operating voltage and a 3.7× reduction in power consumption over existing state-of-the-art.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.234
Teacher spread0.224 · 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