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All-metal AFM probes fabricated from microstructurally tailored Cu–Hf thin films

2009· article· en· W1995572247 on OpenAlexafffund
Erik J. Luber, Brian C. Olsen, Colin Ophus, Velimir Radmilović, David Mitlin

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMaterials scienceNanoindentationCantileverThin filmComposite materialShadow maskStress (linguistics)MicrofabricationOptoelectronicsNanotechnologyOpticsFabrication

Abstract

fetched live from OpenAlex

A growing number of atomic force microscope (AFM) applications make use of metal-coated probes. Probe metallization can cause adverse side-effects and disadvantages such as stress-induced cantilever bending, thermal expansion mismatch, increased tip radius and limited device lifetime due to coating wear. In this study we demonstrate how to overcome these limitations using microstructural design to create a metallic glass thin film alloy, from which monostructural all-metal AFM cantilevers are fabricated. A detailed compositional study of co-sputtered Cu-Hf films is performed using x-ray diffraction (XRD), nanoindentation, four-point probe and in situ multi-beam optical stress sensing (MOSS). Metallic glass Cu(90)Hf(10) films are found to possess an optimal combination of electrical resistivity (96 microOmega cm), nanoindentation hardness (5.2 GPa), ductility and incremental stress. A continuum model is developed which uses measured MOSS data to predict cantilever warping caused by stress gradients generated during film growth. Subsequently, a microfabrication process is developed to create Cu(90)Hf(10) AFM probes. Uncurled, 1 microm thick cantilevers having lengths of 100-400 microm are fabricated, with tip radii ranging from 10 to 40 nm. As a proof of principle, these all-metal Cu-Hf AFM probes are mounted in a commercial AFM and used to successfully image a known test structure.

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 score0.061
Threshold uncertainty score0.938

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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

Citations9
Published2009
Admission routes2
Has abstractyes

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