Early Analysis and Budgeting of Margins and Corners Using Two-Sided Analytical Yield Models
Bibliographic record
Abstract
Manufacturing process variations lead to variability in circuit delay and, if not accounted for, can cause excessive timing yield loss. The familiar traditional approaches to timing verification, such as the use of process corners and predefined timing margins, cannot readily handle within-die variations. Recently, statistical static timing analysis (SSTA) has been proposed as a way to deal with variability. Although many powerful techniques have been proposed, the fact that SSTA requires a significant change of methodology has delayed its wide adoption. In this paper, we propose a framework whereby the familiar concepts of corners and margins, which are generally meaningful at the transistor or cell level, are elevated to the chip level in order to handle within-die variations. This is achieved by using high-level models, such as the generic path model or the generic circuit model with different classes of paths, to represent the behavior of typical designs. These models allow us to determine ldquoyield-specificrdquo margins (setup and hold margins) and virtual corners, which, if applied during standard (deterministic) timing analysis, would guarantee the desired yield. Our framework can be used at an early stage of circuit design and is consistent with traditional timing verification methodology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".