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Record W2897704647 · doi:10.48550/arxiv.1810.04030

A Complete Surface Integral Method for Broadband Modeling of 3D Interconnects in Stratified Media

2018· preprint· en· W2897704647 on OpenAlexaff
Shashwat Sharma, Utkarsh R. Patel, Sean V. Hum, Piero Triverio

Bibliographic record

VenuearXiv (Cornell University) · 2018
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntegral equationElectric-field integral equationSolverMethod of moments (probability theory)ComputationElectrical conductorComputer scienceMathematical analysisRange (aeronautics)Bessel functionAdmittanceFinite element methodElectronic engineeringMathematicsMathematical optimizationAlgorithmPhysicsElectrical engineeringEngineeringElectrical impedance

Abstract

fetched live from OpenAlex

A surface integral equation solver is proposed for fast and accurate simulation of interconnects embedded in stratified media. A novel technique for efficient computation of the multilayer Green's function is proposed. Using the Taylor expansion of Bessel functions, the computation of Sommerfeld integrals during the method of moments procedure is reduced to simple algebraic operations. To model skin effect in conductors, the single-source differential surface admittance operator is extended to conductors in stratified media. To handle large realistic structures, the adaptive integral method is developed for a multilayer environment in a generalized manner that poses no restrictions on layout of conductors, and requires no special grid refinement, unlike previous works. The proposed method is made robust over a wide frequency range with the augmented electric field integral equation. Realistic structures of different shapes and electrical sizes are successfully analyzed over a wide frequency range, and results are validated against a commercial finite element tool.

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 categoriesMeta-epidemiology (narrow)
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 score0.417
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.075
GPT teacher head0.228
Teacher spread0.153 · 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.

Study designSimulation or modeling
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

Citations4
Published2018
Admission routes1
Has abstractyes

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