A High Efficient and Scalable Obstacle-Avoiding VLSI Global Routing Flow
Bibliographic record
Abstract
Routing is a crucial step in the VLSI design flow. With advancements in manufacturing technology, more constraints have emerged in design rules, particularly regarding obstacles during routing, leading to increased routing complexity. Unfortunately, many global routers struggle to generate efficient obstacle-free solutions due to the lack of scalable obstacle-avoiding tree generation methods and the capability to handle modern designs with complex obstacles and nets. In this work, we propose an efficient obstacle-aware global routing flow for VLSI designs with obstacles. The flow includes a rule-based obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) algorithm during the tree generation phase. This algorithm is both scalable and fast, providing tree topologies avoiding obstacles in the early stage globally. With its guidance, in the later stages, the OARSMT-guided and obstacle-aware sparse maze routing are proposed to further minimize obstacle violations and reduce overflow costs. Compared to previously advanced methods on the benchmark with obstacles, our approach successfully eliminates obstacle violations and reduces wirelength and overflow cost, while sacrificing only a limited number of via counts and runtime overhead.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".