Efficient and Exact Diagnosis of Multiple Stuck-At Faults
Bibliographic record
Abstract
A simulation-based approach to multiple stuck-at fault diagnosis is presented. The algorithm works iteratively as it identifies and fault models a single location at a time so that the functionality of the new netlist gradually resembles that of the corrupted one. The method is based on a theoretical result along with a number of heuristics which help avoid the exponential complexity inherent to the problem. Experiments on multiple stuck-at faults confirm its e#ectiveness and accuracy which scales well with increasing number of faults. J. Brandon Liu Andreas Veneris Magdy S. Abadir University of Toronto University of Toronto Motorola Dept ECE Dept ECE and CS 7700 W. Parmer Toronto, ON M5S 3G4 ON M5S 3G4 Austin, TX 78729 liuji@eecg.toronto.edu veneris@eecg.toronto.edu m.abadir@motorola.com 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".