GHz-Range Modeling of Power Integrity in an Array of Simultaneously Switching Power Converters
Bibliographic record
Abstract
Power integrity issues and voltage fluctuations on power rails in emerging system-in-packages (SiP) that integrate switch-mode converters can impede the performances of embedded sensitive analog devices. Indeed, the progress of power device downsizing allows higher switching frequencies and shorter switching times, which create significant high-frequency switching noise in power rails. More specifically for converters that operate under a range of loading, gate driving and biasing conditions, a behavioral model is necessary in the context where simulation only is not sufficient for proper prediction of the power integrity characteristics of a system under that range of conditions. This article proposes a power integrity model and a characterization methodology to predict voltage fluctuations on the power rails in simultaneous switching conditions on an array of switch-mode converters integrated in SiP. Results show that for every condition studied in this work, accuracy better than 6 dB, aside from some limited discrete frequencies, is obtained between$0$and 1.9$\text{G}$$\text{Hz}$when the fluctuations caused by a single or multiple converters are measured. In terms of overall shape, for every condition studied both under simultaneous switching or not, the variance-normalized mean squared error is in a worst case of 0.624 and under numerous conditions better than 0.25.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".