Efficient Parasitic-aware <i> g <sup>m</sup> </i> / <i> I <sup>D</sup> - </i> based Hybrid Sizing Methodology for Analog and RF Integrated Circuits
Bibliographic record
Abstract
As the primary second-order effect, parasitic issues have to be seriously addressed when synthesizing high-performance analog and RF integrated circuits (ICs). In this article, a two-phase hybrid sizing methodology for analog and RF ICs is proposed to take into account parasitic effect in the early design stage. It involves symbolic modeling and mixed-integer nonlinear programming (MINLP) in the first phase, and a many-objective evolutionary algorithm (many-OEA)-based sizing refiner in the second phase. With the aid of our proposed current density factor and piecewise curve fitting technique, the g m / I D concept, which is typically utilized to solve the analog circuit design problem, can provide theoretical support to our accurate symbolic modeling. Thus, the intrinsic and interconnect parasitics can be accurately considered in our work with moderate modeling effort. A variety of electrical, geometric, and parasitic (including parasitic mismatch) constraints can be conveniently integrated into our MINLP problem formulation. Moreover, numerical simulations are embedded into the many-OEA-based sizing phase, which is able to tackle floorplan co-optimization. With such dynamic floorplan variation, the parasitics accuracy can be sustained along the evolution. The experimental results demonstrate high efficacy of our proposed parasitic-aware hybrid sizing methodology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".