Passive Reduction Algorithm for RLC Interconnect Circuits With Embedded State-Space Systems (PRESS)
Bibliographic record
Abstract
With the increasing operating frequencies and functionality in modern designs, the resulting size of circuit equations of high-frequency interconnect and microwave subnetworks are becoming large. Model-order reduction-based algorithms were recently suggested to handle the solution complexity of such circuits. The major objectives in state-of-the-art model-reduction algorithms are: 1) achieving accurate and compact models; 2) numerically stable and efficient generation of models; and 3) preservation of system properties such as passivity. Algorithms such as PRIMA generate guaranteed passive reduced-order models for large interconnect circuits described by RLC type of circuits. However, with the diverse technologies and complex geometries, it is becoming prevalent to describe some of the embedded linear modules in terms of state-space equations. In this paper, we show how to extend the scope of PRIMA-type first-level reduction algorithms for simultaneous reduction of combined circuits containing both RLC interconnects and embedded modules described by general passive state-space equations, while preserving the passivity of the resulting reduced-order model. Necessary formulation, proof of macromodel passivity, and validation examples are given.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".