Analog layout retargeting using geometric programming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To satisfy the requirements of complex and special analog layout constraints, a new analog layout retargeting method is presented in this article. Our approach uses geometric programming (GP) to achieve new technology design rules, implement device symmetry and matching constraints, and manage parasitics optimization. The GP, a class of nonlinear optimization problem, can be transferred or fitted into a convex optimization problem. Therefore, a global optimum solution can be achieved. Moreover, the GP can address problems with large-scale variables and constraints without setting an initialization variable range. To meet the prerequisites of the GP methodology for analog layout automation, we propose three kinds of mathematical transformations, including negative coefficient transformation, fraction transformation, and maximum of posynomial transformation. The efficiency and effectiveness of the proposed algorithm, as compared with the other existing methods, are demonstrated by a basic case-study example: a two-stage Miller-compensated operational amplifier and a single-ended folded cascode operational amplifier.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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