Bibliographic record
Abstract
One of the main challenges facing designers of high-speed integrated circuits and interconnects is predicting the effect of the variability of geometrical and physical parameters on the circuit performance.Monte Carlo (MC) simulation has been traditionally used in commercial circuit and electromagnetic (EM) simulators for predicting the statistical distribution of circuit and system performance metrics.However, the high computational cost associated with the MC simulation is a significant limiting factor especially for large circuits.In this thesis, an algorithm based on Parametric Model Order Reduction (PMOR) is presented for statistical analysis of large microwave and high-speed circuits with multiple stochastic parameters.Using the proposed algorithm, a set of local reducedorder parameterized circuits are derived based on adaptive frequency sampling and implicit multi-moment matching projection techniques.The local models preserve the stochastic parameters as symbolic quantities.As a result, stochastic response of the circuit can be obtained by simulating the local reduced models instead of the original large system leading to significant reduction in the computational cost compared to traditional Monte-Carlo techniques.In addition, a method is presented for high-dimensional variability analysis.This algorithm is based on two main concepts, namely node tearing for parameter partitioning and sparse grid interpolation.Node-tearing is used to localize the parameters Most of the acknowledgments begins with "I would like to acknowledge someone", which sounded like a speech after winning a Nobel prize somehow.To me, pursuing PhD is an enjoyable adventure full of memorial moments.I have known my supervisor Professor Michel Nakhla since my fourth year undergraduate.I took one of his courses and he guided my fourth-year project.During his lecture, he could explain some very difficult concepts clearly with very few words and obscure theories with intuitively clear language, yet profoundly and systematically.I immediately applied to join this research team for graduate study without any hesitation and luckily, I got accepted.From then on, Professor Nakhla opened a new gate for me and led me into the realm of CAD.Pursuing PhD requires innovations, yet not all lead to success.Many PhD students may feel lost and confusing during this thorny path, I was no exception.His encouragement, with only few incisive words, could always clear my negative thoughts completely.Moreover, he cares about my future and always provides guidance and adjustment for further milestones."Learn from the wise" is the exact word.For seven years we spent, apart from the unlimited knowledge he shared with me, the confidence and optimism I built up from him is invaluable lifelong.Meanwhile, Professor Emad Gad showed
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".