An advanced graph-based placement representation for analog layout design
Bibliographic record
Abstract
Due to complexity and susceptibility of analog layouts towards circuit performance, maturity state of analog integrated circuit (IC) physical design automation has largely lagged behind that of the digital counterpart. Placement is an indispensable stage in the analog IC layout design. It demands effective representations to handle nontrivial analog placement topologies especially in the advanced nanometer technologies. In this thesis, we mainly review the existing placement representations and deepen the research of topological representations for the analog placement design. By leveraging the equivalence between sequence pair (SP) and transitive closure graph (TCG), we propose an SP-driven TCG representation and its associated operations to facilitate the handling of analog placement constraints. To achieve the symmetry-aware placement, we introduce a set of special symmetric-feasible conditions and define an efficient construction mechanism for symmetric placement with the SP-driven TCG representation. A set of SP-driven perturbation operations is also brought forth in this thesis to reduce the algorithmic complexity while satisfying symmetry constraints. Furthermore, a redundancy control scheme among the representation states is developed in order to generate high-performance analog placement with high computation efficiency. Based on the SP-driven TCG representation, we further introduce an Advanced Transitive-Closure-Graph-based placement representation (ATCG). It can effectively and efficiently tackle advanced geometric constraints, which are highly essential for addressing layout dependent effects, thermal effects, and diverse parasitic challenges in the advanced nanometer technologies. ATCG not only inherits all the advantage from both SP and TCG, but also resolve the ambiguous diagonal relationship between any two specified modules. The versatility and flexibility of ATCG can ensure it to accurately control spacing and merging constraints uniquely required by analog layout design. We have implemented our proposed placement methods and tested them with several circuits. Our experimental results demonstrate high efficacy of these proposed representations and the developed operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".