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
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.
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.
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 it