Exploiting STI stress for performance
Bibliographic record
Abstract
performance of transistors has been a major industry focus. An intrinsic stress source – shallow trench isolation – has not been fully utilized up to now for circuit performance improvement. In this paper, we present a new methodology that combines detailed placement and active-layer fill insertion to exploit STI stress for performance improvement. We perform process simulation of a production 65nm STI technology to generate mobility and delay impact models for STI stress. Based on these models, we are able to perform STI stress-aware delay analysis of critical paths using SPICE. We then present our timing-driven optimization of STI stress in standard cell designs, using detailed placement perturbation to optimize PMOS performance and activelayer fill insertion to optimize NMOS performance. We assess our optimization on small designs implemented with a 65nm production cell library and a standard synthesis, place and route flow. Our timing-driven optimization of STI stress impacts can improve clock frequency by between 7 % to 11%. The frequency improvement through exploitation of STI stress comes at practically zero cost in terms of design area and wirelength. I.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".