WeSPer: A flexible small delay defect quality metric
Bibliographic record
Abstract
Testing for small delay defects (SDDs) is important due to their dominance in recent technology nodes. Unfortunately, all the SDD test quality metrics in the literature limit their assessment to the size of the delay defect tested under at-speed or slower clocks, which makes their results misleading under special cases such as faster-than-at-speed testing. Moreover, those metrics are inadequate for assessing the quality of recent SDD test methods that consider the variation of delays in a circuit. In this paper, a novel flexible SDD quality metric that can be adapted according to the available information and the applied test method is proposed. The proposed metric is named Weighted Slack Percentage (WeSPer) as it is defined by a slack ratio weighted by confidence level (CL) multipliers. The flexibility comes from the ability to model test inaccuracies or delay varying effects into the CL multipliers. This paper presents the WeSPer metric, along with a CL multiplier that penalizes overtesting to give a more accurate assessment of the quality of faster-than-at-speed testing. The metric is calculated for several benchmark circuits and compared to other SDD metrics found in the literature. The results show that WeSPer is better than other metrics at representing the quality of SDD tests, especially under faster-than-at-speed testing.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| 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) | 0.002 | 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".