Equivalent Circuit Model Development Accounting for Mutual-Coupling Effects
Bibliographic record
Abstract
Mutual-coupling effects are of utmost importance in the development of high-frequency circuits and systems. However, it is a common practice to ignore those couplings when establishing equivalent circuit models. Neglecting these couplings leads to inaccurate circuit modelling. Therefore, it becomes imperative to account for mutual couplings in the development of accurate equivalent circuit models. This work presents a holistic process for synthesizing the equivalent circuit model of an electromagnetic (EM) field structure that incorporates mutual couplings of varying orders. The proposed high-order framework begins by developing equivalent circuit models for each individual transmission line discontinuity within the target circuit. Subsequently, the mutual couplings of different orders are extracted in a step-by-step manner. Throughout this process, full-wave EM simulations are deployed, along with a circuit parameter extraction method that utilizes de-embedded circuit responses. By combining these techniques, a comprehensive and accurate equivalent circuit model is generated, enabling a detailed analysis of the target field model structure, and facilitating a deeper understanding of its electrical and magnetic behavior and performance. This paper utilizes a three-step microstrip discontinuity structure and a thirdorder parallel coupled microstrip filter as examples for theoretical and experimental demonstration of the proposed technique.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".