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Record W2170160201 · doi:10.1109/ccece.2002.1015253

Probe position control for enhanced resolution of electrostatic force microscopy

2003· article· en· W2170160201 on OpenAlexaff
Zifeng Weng, Greg E. Bridges, D. J. Thomson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectrostatic force microscopeVoltageImage resolutionResolution (logic)Position (finance)CapacitanceElectronic circuitMaterials scienceSensitivity (control systems)Scanning probe microscopyOpticsOptoelectronicsPhysicsElectrical engineeringElectrodeNanotechnologyComputer scienceElectronic engineeringAtomic force microscopyEngineering

Abstract

fetched live from OpenAlex

Electrostatic force microscopy (EFM) testing is an extremely useful tool for non-contact internal function and failure analysis of high-speed integrated circuits. Internal circuit voltages are measured by sensing the local electrostatic force on a small micromachined probe that is held in close proximity to the circuit measurement point. Since electrical forces are longrange in nature, the tip-to-sample spacing is usually the determining factor for the EFM instrument spatial resolution and is typically the same order as the spacing. To significantly improve the EFM instrument spatial resolution and voltage sensitivity, a position feedback system, based on controlling the capacitance gradient, is presented to maintain the tip-to-sample spacing to approximately 100 nm. For such small separations, the feedback system prevents the tip from crashing into the surface due to either environmental disturbances or snapping into the surface due to the electrical force gradient.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.273
Teacher spread0.268 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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