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Record W2168959135 · doi:10.1109/81.956013

Exponential expansion for field computation and capacitance extraction

2001· article· en· W2168959135 on OpenAlexaff
Hoan Pham, Arokia Nathan

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · 2001
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMultipole expansionCapacitanceExponential functionComputationComputer scienceElectronic engineeringField (mathematics)Very-large-scale integrationComputational scienceDouble exponential functionAlgorithmPhysicsMathematicsMathematical analysisEngineering

Abstract

fetched live from OpenAlex

We report a new numerical technique based on the exponential expansion of the Green's function 1/r for accurate and rapid computation of the three-dimensional potential field, its gradient, and the charge density distribution needed for capacitance extraction in multiple-dielectric-multiconductor systems. The exponential expansion provides a computational efficient alternative to the commonly used technique based on multipole expansion of spherical harmonics. With exponential expansion, the memory requirement is independent of the desired degree of accuracy and different forms of parallelism are available for both remotely distributed networks and closely coupled parallel systems. These features permit large-scale simulation, involving panel (element) count in the range of a few hundred thousand to several million, needed for extraction of the parasitic coupling capacitance in VLSI interconnects, including large-area amorphous silicon electronics, as well as for analysis of electrostatic interaction in micro-electro-mechanical systems (MEMS).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.601
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.252
Teacher spread0.236 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

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