EA-Based LDE-Aware Fast Analog Layout Retargeting With Device Abstraction
Why this work is in the frame
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Bibliographic record
Abstract
As the technology node continuously scales down, layout-dependent effects (LDEs) have been significantly affecting the threshold voltage and mobility of MOSFET transistors and then, in turn, the performance of analog integrated circuits. In this paper, we propose an LDE optimization methodology based on the evolutionary algorithm, which aims to protect analog circuits from the LDE-induced circuit performance degradation. With the aid of a fast analog layout retargeting scheme, our proposed optimization can evaluate the circuit performance with the consideration of detailed physical layouts, tune the device placement and transistor finger number, and modify the layout patterns for the LDE-aware circuit performance preservation. To accelerate the physical layout synthesis, our new retargeting process supports general device abstraction. The experimental results show that our proposed methodology can more effectively preserve analog and even RF circuit performance with higher efficiency than the alternative approaches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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