Density-Uniformity-Aware Analog Layout Retargeting
Why this work is in the frame
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Bibliographic record
Abstract
To enhance yield of fabricated chips, foundries normally insert dummy features to tape-out-ready layouts in order to improve pattern density uniformity. This process may unfortunately degrade the performance of analog/radio-frequency and high-speed digital integrated circuits. In this paper, we investigate the ways how to control pattern density distribution on different layers in analog layouts during the process of layout migration from an old technology to a new one or for design specification update in the same technology. We develop a new set of schemes for smartly modifying layout elements to improve pattern density uniformity besides the traditional dummy-insertion operation. The experimental results show that our proposed approach can account for up to 80% improvement toward the ideal density uniformity in the regular analog layouts. This promising option can significantly decrease the coupling-capacitance-induced parasitic effects due to the traditional sole dummy-insertion operation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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