A novel hybrid method for SDD pattern grading and selection
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
Small-delay defects (SDDs) have become a major concern in nanometer technology designs. Traditional timing-unaware transition-delay fault (TDF) ATPGs are not efficient in detecting SDDs since they tend to detect delay faults via shorter paths. Timing-aware ATPG tools have been proven to result in significantly large CPU runtime and pattern count. In this paper, we present a hybrid procedure that grades patterns in terms of their effectiveness in detecting SDDs and selects the most effective ones. The grading procedure is performed on a large repository of patterns generated by n-detect TDF ATPG and takes advantage of n-detect capability in detecting a delay fault n times from different paths. 1-detect TDF ATPG is performed after pattern grading and selection to ensure same fault coverage as timingaware ATPG's is obtained. Experimental results demonstrate that our proposed hybrid method is fast and efficient; it can sensitize a greater number of longer paths with much lower pattern count and CPU runtime compared to a commercial timing-aware ATPG tool.
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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