Efficient Macromodel for Interconnects Excited by Incident Fields
Bibliographic record
Abstract
The analysis of interconnects in the presence of incident electromagnetic fields is an important part of the design process. Such an analysis is, however, very CPU expensive due to the large size of interconnects networks. Krylov based reduction methods were proposed in order to address the complexity of such simulations, however, they result in macromodels which are not optimal and still contain many redundant poles. In this paper a two level reduction method is proposed for interconnect affected by incident electromagnetic fields. The macro-models obtained using the proposed approach is less than half the size of those generated by previous techniques. The first level of reduction is a conventional Krylov subspace based reduction, while the second level is based on singular value decomposition. Furthermore, the proposed method is a projection method based on congruence transformation and is therefore passive by construction. Numerical examples are shown in order to illustrate the accuracy and efficiency of the proposed technique
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".