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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0$</tex-math></inline-formula> and 1.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{G}$</tex-math></inline-formula><inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{Hz}$</tex-math></inline-formula> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".