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Record W2092485076 · doi:10.1116/1.1460901

Quantitative voltage measurement of high-frequency internal integrated circuit signals by scanning probe microscopy

2002· article· en· W2092485076 on OpenAlexafffund
Zifeng Weng, C.J. Falkingham, Greg E. Bridges, D. J. Thomson

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsVoltageWidebandAmplifierBandwidth (computing)Materials scienceCalibrationDC biasCantileverElectronic circuitSystem of measurementScanning probe microscopyFrequency modulationAcousticsAmplitudeIntegrated circuitOptoelectronicsOpticsElectrical engineeringPhysicsEngineeringCMOSTelecommunications

Abstract

fetched live from OpenAlex

This article describes a scanning probe microscopy technique for quantitative high-speed voltage wave form measurement inside an operating integrated circuit. Internal signals are determined by sensing the local electrostatic force on a noncontacting micromachined probe cantilever that is closely positioned above the circuit test point. Amplitude modulation based downconversion is employed to measure repetitive high-frequency signals which can have a bandwidth much greater than the mechanical response of the probe. A force-nulling technique is used to obtain accurate absolute voltages without the need for complex calibration or precise probe positioning, and enables direct measurement of passivated circuits. A method of eliminating dc offset errors, such as that due to material work function differences, is described. Measurement of signals on the passivated interconnects of a wideband distributed amplifier is presented. The instrument demonstrates a voltage accuracy of <30 mV over a dynamic range of 2.5 V.

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)0.001
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 score0.071
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.275
Teacher spread0.256 · 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

Citations8
Published2002
Admission routes2
Has abstractyes

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