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CMOS-MEMS dynamic FM atomic force microscope

2013· article· en· W2004313130 on OpenAlexaff
Niladri Sarkar, G. Lee, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Waterloo
FundersDefense Advanced Research Projects Agency
KeywordsPiezoresistive effectChipCantileverMicroelectromechanical systemsCMOSMaterials scienceFrequency modulationAtomic force microscopyModulation (music)OptoelectronicsMicroscopeNon-contact atomic force microscopyElectronic engineeringConductive atomic force microscopyElectrical engineeringRadio frequencyEngineeringOpticsPhysicsAcousticsNanotechnology

Abstract

fetched live from OpenAlex

We present the first imaging results with a dynamic FM-AFM (frequency modulation atomic force microscope) on a chip. This instrument does not require any off-chip scanning hardware or position sensors. The CMOS-MEMS dynamic FM-AFM includes 3-D electrothermal actuation, 3-axis position sensing, and a flexural resonant cantilever with balanced piezoresistive detection, all integrated on a single chip. Several design principles are applied to reduce the instrument's sensitivity to electrical and thermal coupling effects. To the best of the authors' knowledge, this is the first integrated dynamic FM-AFM that can image a sample independently.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.181
Threshold uncertainty score0.999

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)0.0060.002

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 head0.004
GPT teacher head0.251
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2013
Admission routes1
Has abstractyes

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