MétaCan
Menu
Back to cohort
Record W1638909244 · doi:10.1109/ccece.2002.1015268

Sub-zeptofarad sensitivity scanning capacitance microscopy

2003· article· en· W1638909244 on OpenAlexaff
Thị Thu Hường Trần, Derek R. Oliver, D. J. Thomson, Greg E. Bridges

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsResonatorCapacitanceMaterials scienceSense (electronics)Sensitivity (control systems)OptoelectronicsScanning capacitance microscopyNoise (video)VoltageElectrical engineeringQ factorRaster scanOpticsMicroscopyPhysicsElectrodeElectronic engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

The scanning capacitance microscope technique described is based on a dC/dV measurement at the metal-semiconductor junction between a metallic probe and a sample. The probe forms part of a RF resonator and is scanned in a raster across the sample. Changing dopant concentrations in the sample result in small variations in the junction capacitance, changing the load on the resonator. The sensitivity of a capacitance sensor depends on the operating frequency, the quality factor (Q) of the resonator and sense voltage applied to the resonator. Increasing any of these parameters will increase the sensitivity of the instrument. The instrument described in this paper operates at 2.5 GHz and the resonators have Q values in the range 50-100. Importantly, these resonator designs can operate with low sense voltages (0.1 V-1.5 V), minimizing artefacts that result from larger sense voltages. Capacitance noise response and DC stability of the sensor have been used to demonstrate idealized (unloaded) sensitivities as low as 0.71/spl times/10/sup -21/ F//spl radic/Hz.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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 teacher head, 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

Citations6
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicForce Microscopy Techniques and ApplicationsFrench-language works237,207