Placement Algorithm in Analog-Layout Designs
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
Analog macrocell placement is an NP-hard problem. This paper presents an attempt to solve this problem by using the optimization flow of a genetic algorithm (GA) enhanced by simulated annealing (SA). The bit-matrix representation is employed to improve the search efficiency. In particular, to reduce the solution space without degrading search opportunities, the technique of cell slide is deployed to transform an absolute placement to a relative placement. Following this cell-slide process, it is proved that, for an initial placement, there always exists a solution that can guarantee no occurrence of overlaps among cells and meet any applicable symmetry constraints pertaining to analog layouts. For the optimization of the algorithm parameters, the fractional factorial experiment using an orthogonal array has been conducted, and the exact parameter values are determined using a meta-GA approach. The experimental results show that, compared with the SA approach, the proposed algorithm consumes less computation time while generating higher quality layouts, comparable to expert manual placements
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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 | 0.001 | 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