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Record W2168258332 · doi:10.1109/tcpmt.2011.2117423

Efficient Modeling of Power/Ground Planes Using Delay-Extraction-Based Transmission Lines

2011· article· en· W2168258332 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsWestern University
Fundersnot available
KeywordsDiscretizationTransmission linePartial element equivalent circuitElectronic engineeringElectric power transmissionComputer scienceLine (geometry)Power (physics)Emphasis (telecommunications)Transmission (telecommunications)Equivalent circuitTopology (electrical circuits)EngineeringElectrical engineeringVoltageMathematicsTelecommunicationsGeometry

Abstract

fetched live from OpenAlex

This paper presents an efficient approach for modeling irregular shaped power distribution networks (PDN) in high-speed packages. The proposed methodology is based on discretization of the plane into an orthogonal grid of transmission line segments. Using a delay-extraction-based model for each line segment, a compact circuit model is achieved where the size of the circuit matrices depend only on the nodes of the orthogonally discretized structure and all other internal nodes due to the macromodel are eliminated. This approach of eliminating the internal variable due to the transmission line macromodel is further extended to model skin effect losses without augmenting the circuit matrices. The proposed work has been successfully implemented for a variety of PDN structures and geometries and has been shown to yield significant savings in memory and run time costs compared to the existing simulation program with integrated circuits emphasis macromodels.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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