A performance-constrained template-based layout retargeting algorithm for analog integrated circuits
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Bibliographic record
Abstract
Performance of analog integrated circuits is highly sensitive to layout parasitics. This paper presents an improved template-based algorithm that automatically conducts performance-constrained parasitic-aware retargeting and optimization of analog layouts. In order to achieve desired circuit performance, performance sensitivities with respect to layout parasitics are first determined. Then the algorithm applies a piecewise-sensitivity model to control parasitic-related layout geometries by directly constructing a set of performance constraints subject to maximum performance deviation due to parasitics. The formulated problem is finally solved using graph-based techniques combined with mixed-integer nonlinear programming. The proposed method has been incorporated into a parasitic-aware automatic layout optimization and retargeting tool. It has been demonstrated to be effective and efficient especially when adapting layout design for new technologies or updated specifications.
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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 | 0.000 | 0.000 |
| 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