Response data compaction in BIST under generalized mergeability based on switching theory formulation and utilizing a new measure of failure probability.
Bibliographic record
Abstract
The present thesis deals with the general problem of designing and analyzing efficient space compression techniques for built-in self-testing of VLSI circuits using compact test sets. The techniques are based on identifying certain inherent properties of the test data responses of the CUT along with the knowledge of nonoccurrence of failure probabilities. To that effect, generalized mergeability criteria are developed in the thesis that utilize the well known switching theory concepts of Hamming distance, cover table, and frequency ordering of literals in conjunction with those of sequence weights (first-order and Nth-order) and derived sequences. The thesis also explores the effect on sequence mergeability under constraints of stochastic independence of multiple line errors and its outcome on the fault coverage. Extensive simulation experiments on ISCAS 85 combinational benchmark circuits with FSIM, ATALANTA, and COMPACTEST programs indicate that the proposed techniques achieve a relatively high fault coverage for single stuck-line faults with low CPU simulation time and acceptable area overhead for the designed compactors. The subject thesis also rates the performance of the designed compactors with that of the conventional linear parity tree space compactors. (Abstract shortened by UMI.)
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".