NIM-X: A Noise Index Model-Based X-Filling Technique to Overcome the Power Supply Switching Noise Effects on Path Delay Test
Bibliographic record
Abstract
Power supply noise (PSN) has become a critical issue during high-quality at-speed testing. Discrepancies between the circuit's switching activity during functional and test mode can cause overtesting and lead to yield loss. Alternatively, reduced PSN effects around critical paths can result in undertesting the chip, causing test escapes. To achieve a high-quality at-speed test, it is necessary to solve these problems simultaneously. Our previous work introduced a noise index model (NIM), which can be used to predict the mismatch between expected and real path delays. This paper quantitatively investigates and compares NIM values for critical paths during functional and test mode. We then propose a test pattern modification method that harnesses the NIM. The method fills a subset of the don't care bits in partially specified test vectors such that the worst observed functional NIM for the targeted critical path is replicated during test mode.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".