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Record W2078226513 · doi:10.1109/tbcas.2014.2336596

A New Fully Differential CMOS Capacitance to Digital Converter for Lab-on-Chip Applications

2014· article· en· W2078226513 on OpenAlexafffund
Ghazal Nabovati, Ebrahim Ghafar‐Zadeh, Maryam Mirzaei, Giancarlo Ayala‐Charca, Falah Awwad, Mohamad Sawan

Bibliographic record

VenueIEEE Transactions on Biomedical Circuits and Systems · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsYork UniversityPolytechnique Montréal
FundersCanada Research Chairs
KeywordsCapacitanceDifferential capacitanceCMOSCapacitive sensingSensitivity (control systems)ChipElectronic engineeringMaterials scienceDynamic rangeElectrical engineeringMicroelectrodeOptoelectronicsEngineeringCapacitorPhysicsVoltageElectrode

Abstract

fetched live from OpenAlex

In this paper, we present a new differential CMOS capacitive sensor for Lab-on-Chip applications. The proposed integrated sensor features a DC-input ΣΔ capacitance to digital converter (CDC) and two reference and sensing microelectrodes integrated on the top most metal layer in 0.35 μm CMOS process. Herein, we describe a readout circuitry with a programmable clocking strategy using a Charge Based Capacitance Measurement technique. The simulation and experimental results demonstrate a high capacitive dynamic range of 100 fF-110 fF, the sensitivity of 350 mV/fF and the minimum detectable capacitance variation of as low as 10 aF. We also demonstrate and discuss the use of this device for environmental applications through various chemical solvents.

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 score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.016
GPT teacher head0.225
Teacher spread0.210 · 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

Citations29
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Biomedical Circuits and SystemsSame topicAnalytical Chemistry and SensorsFrench-language works237,207