Symmetry‐aware placement algorithm using transitive closure graph representation for analog integrated circuits
Bibliographic record
Abstract
Abstract Recently several topological representations have been explored as alternatives to the conventional absolute‐coordinate representation for integrated circuit layout automation. Those topological representations, however, lack one or more aspects in capturing the solution space subject to symmetry constraints, which are abundant in analog layouts. In this paper, we explore the use of transitive closure graphs (TCGs) to represent analog placements, i.e. placements with symmetry constraints. We define a set of conditions so that a TCG satisfying these conditions, referred to as a symmetric‐feasible TCG , will correspond to a valid symmetric placement and vice versa. We then present an O ( n 2 ) algorithm, where n is the number of cells to be placed, to build a symmetric placement from a symmetric‐feasible TCG, a problem known as packing. We further describe a set of random perturbation operations on existing symmetric‐feasible TCGs to generate new symmetric‐feasible TCGs with time complexity of O ( n ) . This allows our TCG‐based symmetry‐aware analog placer to search only the symmetric‐feasible TCG solution space, leading to a substantial reduction of the search space and solution time. Experimental results on several analog circuits have confirmed the superiority of the TCG representation to the conventional absolute‐coordinate representation as well as several other topological representations in analog layout design. Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".