Chip-package power delivery network resonance analysis and co-design using time and frequency domain analysis techniques
Bibliographic record
Abstract
Traditional methods of performing worst-case DC or static analysis serves limited purposes for power delivery network (PDN) validation, especially when it comes to modeling chip-package-PCB coupling or resonance behavior. These methods do not consider the inductive and capacitive elements that dominate the chip and package interaction. They also fail to capture the impact of simultaneous switching current in creating local hot-spots and global voltage rail collapse. In this study, an analysis methodology that combines the use of both time and frequency domain techniques to model the impact of Ldi/dt noise and the coupling of chip-level switching current with chip-package impedance is presented. The outlined techniques were used on a design targeting high-speed signal processing applications to identify resonance behavior of chip-package PDN systems. Simulations were performed on various configurations of the design to ensure that the proposed design changes would correct the resonance and other PDN related issues. The analysis flow, information on the various data used, run-time and performance statistics, and the results from these experiments are presented.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".