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
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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 g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> / I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> -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 g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> / I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> design 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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it