Power Supply Induced Jitter Including the Ground Bounce and Transmission Media Effects
Bibliographic record
Abstract
In this thesis, an efficient method for estimation of power supply induced jitter (PSIJ) in high-speed designs is presented.EMPSIJ [34] method is advanced to handle the combined effects of both the transmission media and ground bounce in the presence of supply noise.Semi-analytical relations are developed based on small-signal noise analysis for quick estimation of PSIJ and one bit simulation of the large signal model.For this purpose, small-signal configuration of a voltage-mode driver circuit is considered to evaluate the differential output response.Also, an alternative and systematic approach is proposed based on MNA tridiagonal formulation and Thomas Algorithm for PSIJ analysis.It avoids re-derivation of the expressions in the case of any change in load conditions.Also, a novel closed-form model for transmission line type interconnects is developed relating the input and the output nodes of the transmission line which enables efficient PSIJ analysis in the presence of PCB traces.The proposed closed-form expression for transmission line type interconnects is also advanced to include the effect of load.Several validating examples are presented for the proposed approaches using different types of noise sources.The results are compared with HSPICE simulator to validate the accuracy and efficiency.i My sincere gratitudes to my thesis supervisor Prof. R. Achar, for his patience, motivation and immense knowledge.Without his precious support and guidance, it would not be possible to conduct this research.His office door was always open
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".