An LDE-Aware <i>g</i> <sub> <i>m</i> </sub>/<i>I</i> <sub> <i>D</i> </sub>-Based Hybrid Sizing Method for Analog Integrated Circuits
Bibliographic record
Abstract
Layout-dependent effects (LDEs) have become increasingly more important in the synthesis of analog integrated circuits. In this article, a two-phase hybrid sizing method for high-performance analog circuits is proposed. It consists of gm/ ID-based device characterization, circuit modeling, sensitivity-based constraints for LDEs, mixed-integer nonlinear programming (MINLP) in the first phase, and many-objective evolutionary algorithm (many-OEA)-based sizing in the second phase. In the first phase, accurate device characterization is handled with little modeling effort thanks to the gm/ IDdesign methodology. Then, the LDE parameters that are linked to the normalized dc current are further optimized with the aid of sensitivity analysis. Thus, a variety of electrical, geometrical, and LDE-related constraints can be conveniently integrated into modeling of the sizing problem. In the second phase, the many-OEA-based sizing refiner can further optimize the LDE parameters by using more detailed layout information via our proposed model. A new floorplan variation scheme is also applied to improve computation efficiency and enhance optimization effectiveness. The experimental results demonstrate high efficacy of our proposed methodology in LDE-aware analog sizing optimization.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".