Deviation-Based LFSR Reseeding for Test-Data Compression
Bibliographic record
Abstract
Linear feedback shift register (LFSR) reseeding forms the basis for many test-compression solutions. A seed can be computed for each test cube by solving a system of linear equations based on the feedback polynomial of the LFSR. Despite the availability of numerous LFSR-reseeding-based compression methods in the literature, relatively little is known about the effectiveness of these seeds for unmodeled defects, particularly since there are often several candidate seeds for a test cube. We use the recently proposed output deviation measure of the resulting patterns as a metric to select appropriate LFSR seeds. Experimental results are reported using test patterns for stuck-at and transition faults derived from selected seeds for the ISCAS-89 and the IWLS-05 benchmark circuits. These patterns achieve higher coverage for transition and stuck-open faults than patterns obtained using other seed-generation methods for LFSR reseeding. Given a pattern pair (p1,p2) for transition faults, we also examine the transition-fault coverage for launch on capture by usingp1andp2to separately compute output deviations. Results show thatp1tends to be better when there is a high proportion of do-not-care bits in the test cubes, whilep2is a more appropriate choice when the transition-fault coverage is high.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".