MétaCan
Menu
Back to cohort
Record W2117886869 · doi:10.1109/modsym.2006.365209

Modeling RF Signal Propagation Along On-Chip Interconnects and the Effect of Substrate Doping with the Alternating-Direction-Implicit Finite-Difference Time-Domain (ADI-FDTD) Method

2006· article· en· W2117886869 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

VenueProceedings of the International Power Modulator Symposium and High Voltage Workshop/Proceedings of the ... International Power Modulator Symposium and ... High Voltage Workshop · 2006
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFinite-difference time-domain methodSubstrate (aquarium)Materials scienceAlternating direction implicit methodDopingSIGNAL (programming language)Finite difference methodOptoelectronicsNoise (video)Electronic engineeringOpticsPhysicsComputer scienceMathematical analysisMathematicsEngineering

Abstract

fetched live from OpenAlex

The alternating-direction-implicit finite-difference time-domain (ADI-FDTD) method is used to analyze metal-insulator-semiconductor-metal interconnects by solving Maxwell's equations in the time domain. This analysis shows that the silicon substrate losses and the metal line losses can be modeled with high resolution. Our modeling method is supported by experimental data. We find that semiconductors readily operate in the slow wave mode and skin-effect mode for selected doping densities. The ADI-FDTD method is also applied to study the effect of epitaxial layers in different propagating modes. Simulation indicates that inserting epitaxial layers in highly doped substrates should help to keep the signal integrities and reduce substrate noise

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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