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Record W2023108796 · doi:10.1109/mwscas.2006.382118

A Delay Characterization Method for Integrated Devices

2006· article· en· W2023108796 on OpenAlexafffund
L.-P. Lafrance, Yvon Savaria

Bibliographic record

VenueConference proceedings · 2006
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsPolytechnique Montréal
FundersCanada Research ChairsCMC Microsystems
KeywordsResistorCapacitorChipCapacitanceRing oscillatorComputer scienceElectronic engineeringCMOSTransistorCalibrationParasitic capacitanceCharacterization (materials science)Process (computing)Electrical engineeringMaterials scienceEngineeringVoltageTelecommunications

Abstract

fetched live from OpenAlex

We present in this paper a new approach for on-chip delay characterization of integrated passive and active devices such as transistors, interconnects and analog resistors and capacitors. The proposed method is a time-domain technique that uses on-chip configurable ring oscillators to characterize independently dynamic parasitic resistance and capacitance of the device under characterization. With the use of empirical techniques, based on interpolated look-up tables of pre-simulated data, the method achieves very good estimates of capacitance and resistance. A calibration technique to compensate process variations is also proposed and validated. The proposed method is implemented on a chip, designed in the CMOS 180nm TSMC technology. The chip was manufactured and is currently under verification.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2006
Admission routes2
Has abstractyes

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