An Effective Global Approach for Assessment of Decoupling Capacitors on Mixed Planar and Transmission Line PDNs
Bibliographic record
Abstract
A global analysis technique is proposed to calculate the effectiveness of decoupling capacitors on practical power delivery networks (PDN). The proposed method is based on separation of a PDN into its power transmission lines (PTL) and non-PTL sections. The PTL section consists of circuit components with the highest impact on the impedance of the specified power pin, including the pin itself, the nearest capacitor and segments of PTL on both sides of the capacitor. The rest of the PDN makes up the non-PTL section which could be composed of planar shapes, PTLs or a mixture of both. The non-PTL section is characterized as a distributed circuit, preferably using an electromagnetic (EM) simulator. The effectiveness of the capacitor is measured by the self-impedance of the pin which depends on the distance between them. The pin impedance is cast in a transcendental equation in the PTL section including the impedance of the non-PTL section. The optimal placement of the capacitor is calculated using an iterative approach. With the proposed method, the use of an EM simulation at each step of the iteration is eliminated, significantly speeding up the computation process. The proposed method is validated on real-life design cases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".